Wrapper_Feature_Selection.ipynb

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      "source": [
        "# Feature selection\n",
        "\n",
        "Feature selection is the process of selecting a subset of relevant features from a larger set to improve model performance or reduce computational complexity. Types of feature selection refer to various methodologies or approaches used to select a subset of features from the original feature set. In this section we will talk about the wrapper feature selection method."
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      "source": [
        "# Wrapper Methods\n",
        "Wrapper methods are a class of feature selection techniques in machine learning where subsets of features are evaluated using a predictive model to identify the best-performing subset. These methods explore different combinations of features using a specific algorithm (wrapper), assessing each combination's performance based on a chosen criterion. The process continues until the algorithm identifies the optimal subset maximizing model performance. Although computationally expensive compared to filter methods, wrapper methods often yield superior results by considering feature interactions and overall model performance. These methodologies can be broadly categorized into five main types:\n",
        "\n",
        "1. Sequnetial Forward Feature Selection\n",
        "2. Sequnetial Forward Floating Feature Selection\n",
        "3. Sequnetial Backward Feature Selection\n",
        "4. Sequnetial Backward Floating Feature Selection\n",
        "5. Exhaustive Feature Selection"
      ],
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      "cell_type": "markdown",
      "source": [
        "# Import Necessary Libraries"
      ],
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      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "pd.set_option(\"display.max_columns\", None)\n",
        "pd.set_option(\"display.max_rows\", None)\n",
        "pd.set_option(\"display.width\", 500)\n",
        "pd.set_option(\"display.float_format\", lambda x: \"%.4f\" % x)\n",
        "\n",
        "!pip install mlxtend\n",
        "import matplotlib.pyplot as plt\n",
        "from sklearn.model_selection import train_test_split\n",
        "from mlxtend.feature_selection import SequentialFeatureSelector\n",
        "from mlxtend.feature_selection import ExhaustiveFeatureSelector\n",
        "from sklearn.preprocessing import OrdinalEncoder\n",
        "from sklearn.preprocessing import normalize\n",
        "from sklearn.ensemble import RandomForestClassifier\n",
        "from sklearn.tree import DecisionTreeClassifier\n",
        "import warnings\n",
        "warnings.filterwarnings(\"ignore\")"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Import Dataset\n",
        "\n",
        "**Dataset Story**: This dataset is originally from the National Institute of Diabetes and Digestive and KidneyDiseases. The objective of the dataset is to diagnostically predict whether a patient has diabetes,based on certain diagnostic measurements included in the dataset. Several constraints were placedon the selection of these instances from a larger database. In particular, all patients here are femalesat least 21 years old of Pima Indian heritage.2From the data set in the (.csv) File We can find several variables, some of them are independent(several medical predictor variables) and only one target dependent variable (Outcome).\n",
        "\n",
        "\n",
        "\n"
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        "dir = \"/content/drive/MyDrive/Colab Notebooks/datasets/diabetes.csv\"\n",
        "df = pd.read_csv(dir)\n",
        "df.head()"
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              "   Pregnancies  Glucose  BloodPressure  SkinThickness  Insulin     BMI  DiabetesPedigreeFunction  Age  Outcome\n",
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              "3            1       89             66             23       94 28.1000                    0.1670   21        0\n",
              "4            0      137             40             35      168 43.1000                    2.2880   33        1"
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              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 768,\n  \"fields\": [\n    {\n      \"column\": \"Pregnancies\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 3,\n        \"min\": 0,\n        \"max\": 17,\n        \"num_unique_values\": 17,\n        \"samples\": [\n          6,\n          1,\n          3\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Glucose\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 31,\n        \"min\": 0,\n        \"max\": 199,\n        \"num_unique_values\": 136,\n        \"samples\": [\n          151,\n          101,\n          112\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"BloodPressure\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 19,\n        \"min\": 0,\n        \"max\": 122,\n        \"num_unique_values\": 47,\n        \"samples\": [\n          86,\n          46,\n          85\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"SkinThickness\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 15,\n        \"min\": 0,\n        \"max\": 99,\n        \"num_unique_values\": 51,\n        \"samples\": [\n          7,\n          12,\n          48\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Insulin\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 115,\n        \"min\": 0,\n        \"max\": 846,\n        \"num_unique_values\": 186,\n        \"samples\": [\n          52,\n          41,\n          183\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"BMI\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 7.884160320375446,\n        \"min\": 0.0,\n        \"max\": 67.1,\n        \"num_unique_values\": 248,\n        \"samples\": [\n          19.9,\n          31.0,\n          38.1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"DiabetesPedigreeFunction\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.3313285950127749,\n        \"min\": 0.078,\n        \"max\": 2.42,\n        \"num_unique_values\": 517,\n        \"samples\": [\n          1.731,\n          0.426,\n          0.138\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Age\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 11,\n        \"min\": 21,\n        \"max\": 81,\n        \"num_unique_values\": 52,\n        \"samples\": [\n          60,\n          47,\n          72\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Outcome\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          0,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 40
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Data Preprocessing"
      ],
      "metadata": {
        "id": "UYr9glxBbOKV"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def load_dataset(dir, drop_labels, target):\n",
        "  df = pd.read_csv(dir)\n",
        "  X = df.drop(labels=drop_labels, axis=1)\n",
        "  y = df[target]\n",
        "  return X, y"
      ],
      "metadata": {
        "id": "qthQnMtbYZyW"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "dir = \"/content/drive/MyDrive/Colab Notebooks/datasets/diabetes.csv\"\n",
        "X, y = load_dataset(dir, drop_labels=[\"Outcome\"], target=\"Outcome\")"
      ],
      "metadata": {
        "id": "7vOmSo6AY4Zh"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "fqRFyoRRY9-0",
        "outputId": "dc0db8f8-469a-4d3d-c47e-a6df29fc7d16"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   Pregnancies  Glucose  BloodPressure  SkinThickness  Insulin     BMI  DiabetesPedigreeFunction  Age\n",
              "0            6      148             72             35        0 33.6000                    0.6270   50\n",
              "1            1       85             66             29        0 26.6000                    0.3510   31\n",
              "2            8      183             64              0        0 23.3000                    0.6720   32\n",
              "3            1       89             66             23       94 28.1000                    0.1670   21\n",
              "4            0      137             40             35      168 43.1000                    2.2880   33"
            ],
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              "    <tr>\n",
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              "      <td>35</td>\n",
              "      <td>168</td>\n",
              "      <td>43.1000</td>\n",
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              "        document.querySelector('#df-464ddb7a-a789-4f65-bbdc-b1cc0bd7b225 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
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              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-464ddb7a-a789-4f65-bbdc-b1cc0bd7b225');\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",
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              "\n",
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              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
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              "      --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",
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              "      --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",
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              "    }\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",
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              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-1536cfad-f49d-4395-991f-27a857e63111 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
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              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "X",
              "summary": "{\n  \"name\": \"X\",\n  \"rows\": 768,\n  \"fields\": [\n    {\n      \"column\": \"Pregnancies\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 3,\n        \"min\": 0,\n        \"max\": 17,\n        \"num_unique_values\": 17,\n        \"samples\": [\n          6,\n          1,\n          3\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Glucose\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 31,\n        \"min\": 0,\n        \"max\": 199,\n        \"num_unique_values\": 136,\n        \"samples\": [\n          151,\n          101,\n          112\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"BloodPressure\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 19,\n        \"min\": 0,\n        \"max\": 122,\n        \"num_unique_values\": 47,\n        \"samples\": [\n          86,\n          46,\n          85\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"SkinThickness\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 15,\n        \"min\": 0,\n        \"max\": 99,\n        \"num_unique_values\": 51,\n        \"samples\": [\n          7,\n          12,\n          48\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Insulin\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 115,\n        \"min\": 0,\n        \"max\": 846,\n        \"num_unique_values\": 186,\n        \"samples\": [\n          52,\n          41,\n          183\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"BMI\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 7.884160320375446,\n        \"min\": 0.0,\n        \"max\": 67.1,\n        \"num_unique_values\": 248,\n        \"samples\": [\n          19.9,\n          31.0,\n          38.1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"DiabetesPedigreeFunction\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.3313285950127749,\n        \"min\": 0.078,\n        \"max\": 2.42,\n        \"num_unique_values\": 517,\n        \"samples\": [\n          1.731,\n          0.426,\n          0.138\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Age\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 11,\n        \"min\": 21,\n        \"max\": 81,\n        \"num_unique_values\": 52,\n        \"samples\": [\n          60,\n          47,\n          72\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 43
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "AnpC7S1LZS0L",
        "outputId": "ea04e4dc-d6d6-4f0a-c9b7-6ce16430dd0f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0    1\n",
              "1    0\n",
              "2    1\n",
              "3    0\n",
              "4    1\n",
              "Name: Outcome, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 44
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y.isnull().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "PZLHDUuZZUlQ",
        "outputId": "50cd4fb0-3f99-4ac8-93dc-72df20e9a9f5"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0"
            ]
          },
          "metadata": {},
          "execution_count": 45
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y.isnull().sum().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hZMeUVSTZZXs",
        "outputId": "387ef6a2-70e1-424f-c625-e5d432cc6214"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0"
            ]
          },
          "metadata": {},
          "execution_count": 46
        }
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    {
      "cell_type": "code",
      "source": [
        "zero_columns = [col for col in X.columns if (X[col].min() == 0 and col not in [\"Pregnancies\"])]\n",
        "for col in zero_columns:\n",
        "  X[col] = np.where(X[col] == 0, np.nan, X[col])"
      ],
      "metadata": {
        "id": "X1q7DYEclbgJ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X.isnull().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "OTs9dBJ6lnqd",
        "outputId": "5ce54748-9dbc-42ad-b8aa-b49fe8a170c4"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Pregnancies                   0\n",
              "Glucose                       5\n",
              "BloodPressure                35\n",
              "SkinThickness               227\n",
              "Insulin                     374\n",
              "BMI                          11\n",
              "DiabetesPedigreeFunction      0\n",
              "Age                           0\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 48
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def fill_na_with_median(dataframe):\n",
        "  dataframe = dataframe.apply(lambda x: x.fillna(x.median()) if x.dtype not in [\"category\", \"object\", \"bool\"] else x, axis=0)\n",
        "  return dataframe"
      ],
      "metadata": {
        "id": "HHP56t1hlw8E"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df_without_nan = fill_na_with_median(X)"
      ],
      "metadata": {
        "id": "XwLqJBK0Z07s"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df_without_nan.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "Qqdb2gM5Z8KW",
        "outputId": "b25ef917-26c9-4320-dbf7-7e7b8902474d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   Pregnancies  Glucose  BloodPressure  SkinThickness  Insulin     BMI  DiabetesPedigreeFunction  Age\n",
              "0            6 148.0000        72.0000        35.0000 125.0000 33.6000                    0.6270   50\n",
              "1            1  85.0000        66.0000        29.0000 125.0000 26.6000                    0.3510   31\n",
              "2            8 183.0000        64.0000        29.0000 125.0000 23.3000                    0.6720   32\n",
              "3            1  89.0000        66.0000        23.0000  94.0000 28.1000                    0.1670   21\n",
              "4            0 137.0000        40.0000        35.0000 168.0000 43.1000                    2.2880   33"
            ],
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              "      <td>2.2880</td>\n",
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              "        const element = document.querySelector('#df-a407ca3a-b148-4101-8fc5-a39ae89ace0a');\n",
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              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
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              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
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              "\n",
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              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
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              "            'suggestCharts', [key], {});\n",
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              "        console.error('Error during call to suggestCharts:', error);\n",
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              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
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              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
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              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df_without_nan",
              "summary": "{\n  \"name\": \"df_without_nan\",\n  \"rows\": 768,\n  \"fields\": [\n    {\n      \"column\": \"Pregnancies\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 3,\n        \"min\": 0,\n        \"max\": 17,\n        \"num_unique_values\": 17,\n        \"samples\": [\n          6,\n          1,\n          3\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Glucose\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 30.43828582241517,\n        \"min\": 44.0,\n        \"max\": 199.0,\n        \"num_unique_values\": 135,\n        \"samples\": [\n          135.0,\n          134.0,\n          86.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"BloodPressure\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 12.096641733978139,\n        \"min\": 24.0,\n        \"max\": 122.0,\n        \"num_unique_values\": 46,\n        \"samples\": [\n          102.0,\n          85.0,\n          86.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"SkinThickness\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 8.791221023089706,\n        \"min\": 7.0,\n        \"max\": 99.0,\n        \"num_unique_values\": 50,\n        \"samples\": [\n          36.0,\n          12.0,\n          28.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Insulin\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 86.383059693181,\n        \"min\": 14.0,\n        \"max\": 846.0,\n        \"num_unique_values\": 185,\n        \"samples\": [\n          70.0,\n          18.0,\n          274.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"BMI\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 6.875176818080995,\n        \"min\": 18.2,\n        \"max\": 67.1,\n        \"num_unique_values\": 247,\n        \"samples\": [\n          19.9,\n          31.0,\n          38.1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"DiabetesPedigreeFunction\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.3313285950127749,\n        \"min\": 0.078,\n        \"max\": 2.42,\n        \"num_unique_values\": 517,\n        \"samples\": [\n          1.731,\n          0.426,\n          0.138\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Age\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 11,\n        \"min\": 21,\n        \"max\": 81,\n        \"num_unique_values\": 52,\n        \"samples\": [\n          60,\n          47,\n          72\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 51
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Encoding and Normalize Data"
      ],
      "metadata": {
        "id": "fWV7r-VsbSN3"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def Encoder(data):\n",
        "    feature_names = data.columns.tolist()\n",
        "    data_encoder = OrdinalEncoder()\n",
        "    input_encoded = data_encoder.fit_transform(data)\n",
        "    input_normalized = normalize(input_encoded, axis=0)\n",
        "    input_normalized_df = pd.DataFrame(input_normalized, columns=feature_names)\n",
        "    return input_normalized_df\n"
      ],
      "metadata": {
        "id": "WcKbYIpvsS2m"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "input = Encoder(df_without_nan)\n",
        "input.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "CflBRaOGpORa",
        "outputId": "342af683-c1cb-4143-c6c9-ad12db65ec6e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   Pregnancies  Glucose  BloodPressure  SkinThickness  Insulin    BMI  DiabetesPedigreeFunction    Age\n",
              "0       0.0424   0.0465         0.0337         0.0430   0.0324 0.0347                    0.0466 0.0618\n",
              "1       0.0071   0.0120         0.0289         0.0335   0.0324 0.0174                    0.0261 0.0213\n",
              "2       0.0565   0.0657         0.0257         0.0335   0.0324 0.0083                    0.0490 0.0234\n",
              "3       0.0071   0.0142         0.0289         0.0239   0.0256 0.0216                    0.0071 0.0000\n",
              "4       0.0000   0.0405         0.0048         0.0430   0.0424 0.0592                    0.0684 0.0256"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-dad87e9f-0a81-42aa-bae9-06ae4525820d\" class=\"colab-df-container\">\n",
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              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
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              "\n",
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              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Pregnancies</th>\n",
              "      <th>Glucose</th>\n",
              "      <th>BloodPressure</th>\n",
              "      <th>SkinThickness</th>\n",
              "      <th>Insulin</th>\n",
              "      <th>BMI</th>\n",
              "      <th>DiabetesPedigreeFunction</th>\n",
              "      <th>Age</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0.0424</td>\n",
              "      <td>0.0465</td>\n",
              "      <td>0.0337</td>\n",
              "      <td>0.0430</td>\n",
              "      <td>0.0324</td>\n",
              "      <td>0.0347</td>\n",
              "      <td>0.0466</td>\n",
              "      <td>0.0618</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0.0071</td>\n",
              "      <td>0.0120</td>\n",
              "      <td>0.0289</td>\n",
              "      <td>0.0335</td>\n",
              "      <td>0.0324</td>\n",
              "      <td>0.0174</td>\n",
              "      <td>0.0261</td>\n",
              "      <td>0.0213</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0.0565</td>\n",
              "      <td>0.0657</td>\n",
              "      <td>0.0257</td>\n",
              "      <td>0.0335</td>\n",
              "      <td>0.0324</td>\n",
              "      <td>0.0083</td>\n",
              "      <td>0.0490</td>\n",
              "      <td>0.0234</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0.0071</td>\n",
              "      <td>0.0142</td>\n",
              "      <td>0.0289</td>\n",
              "      <td>0.0239</td>\n",
              "      <td>0.0256</td>\n",
              "      <td>0.0216</td>\n",
              "      <td>0.0071</td>\n",
              "      <td>0.0000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0.0000</td>\n",
              "      <td>0.0405</td>\n",
              "      <td>0.0048</td>\n",
              "      <td>0.0430</td>\n",
              "      <td>0.0424</td>\n",
              "      <td>0.0592</td>\n",
              "      <td>0.0684</td>\n",
              "      <td>0.0256</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-dad87e9f-0a81-42aa-bae9-06ae4525820d')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\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-dad87e9f-0a81-42aa-bae9-06ae4525820d 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-dad87e9f-0a81-42aa-bae9-06ae4525820d');\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-bd4e007a-657c-4889-a263-11c0b267830c\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-bd4e007a-657c-4889-a263-11c0b267830c')\"\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",
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              "    </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-bd4e007a-657c-4889-a263-11c0b267830c button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "input",
              "summary": "{\n  \"name\": \"input\",\n  \"rows\": 768,\n  \"fields\": [\n    {\n      \"column\": \"Pregnancies\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.023774040372549658,\n        \"min\": 0.0,\n        \"max\": 0.11305232751635282,\n        \"num_unique_values\": 17,\n        \"samples\": [\n          0.04239462281863231,\n          0.007065770469772051,\n          0.021197311409316154\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Glucose\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.01648501304824105,\n        \"min\": 0.0,\n        \"max\": 0.07331107963697348,\n        \"num_unique_values\": 135,\n        \"samples\": [\n          0.039391027864642464,\n          0.038843930255411316,\n          0.012583245012316342\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"BloodPressure\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.011773634365888085,\n        \"min\": 0.0,\n        \"max\": 0.07218020194489662,\n        \"num_unique_values\": 46,\n        \"samples\": [\n          0.0625561750189104,\n          0.04651613014226671,\n          0.04812013462993108\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"SkinThickness\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.013378488284301373,\n        \"min\": 0.0,\n        \"max\": 0.07806398634638693,\n        \"num_unique_values\": 50,\n        \"samples\": [\n          0.04460799219793539,\n          0.00637257031399077,\n          0.03186285156995385\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Insulin\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.013754196929894593,\n        \"min\": 0.0,\n        \"max\": 0.07731463022123905,\n        \"num_unique_values\": 185,\n        \"samples\": [\n          0.016387340101240884,\n          0.0012605646231723757,\n          0.059666725496825786\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"BMI\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.01653561675156346,\n        \"min\": 0.0,\n        \"max\": 0.07001473581379122,\n        \"num_unique_values\": 247,\n        \"samples\": [\n          0.001992289230473734,\n          0.02903050021547441,\n          0.04753032878415908\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"DiabetesPedigreeFunction\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.019028525359130605,\n        \"min\": 0.0,\n        \"max\": 0.06864703931187045,\n        \"num_unique_values\": 517,\n        \"samples\": [\n          0.06784881792452312,\n          0.03259403998334934,\n          0.0035919962430629887\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Age\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.024968490809744736,\n        \"min\": 0.0,\n        \"max\": 0.10871877634357766,\n        \"num_unique_values\": 52,\n        \"samples\": [\n          0.08313788779214762,\n          0.05542525852809842,\n          0.10658703563095849\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 53
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Split Dataset"
      ],
      "metadata": {
        "id": "eQo1kzUzq-pI"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def create_train_test(input, target, test_size=0.33):\n",
        "  x_train, x_test, y_train, y_test = train_test_split(input, target, test_size=test_size, random_state=1)\n",
        "  return x_train, x_test, y_train, y_test"
      ],
      "metadata": {
        "id": "EbKIuCtNqtD3"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "x_train, x_test, y_train, y_test = create_train_test(input, y)"
      ],
      "metadata": {
        "id": "iLxJe6V6rSOy"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Sequential Feature Selection\n",
        "Sequential feature selection is a technique used to determine the most suitable feature subset for a machine learning model. In this method, subsets of features are sequentially evaluated, and at each step, the features that provide the best performance are selected. Model performance is continuously assessed by adding or removing features, ultimately determining the optimal feature subset."
      ],
      "metadata": {
        "id": "g1ig5u0osZXg"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "model_wrapper = SequentialFeatureSelector(RandomForestClassifier(),\n",
        "                                          k_features= 5, forward= True)\n",
        "mymodel = model_wrapper.fit(x_train, y_train)\n",
        "list(mymodel.k_feature_idx_)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "25IAmLBFrXs_",
        "outputId": "6635d448-d946-4ac0-e5e1-1cde93ff92de"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[0, 1, 2, 5, 6]"
            ]
          },
          "metadata": {},
          "execution_count": 56
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def SequentialFeatureSelectionWarpper(classifier, k_features, x_train, y_train, scoring=\"roc_auc\", forward=True):\n",
        "    # Feature selection\n",
        "    model_wrapper = SequentialFeatureSelector(classifier, k_features, scoring=scoring, forward=forward)\n",
        "    mymodel = model_wrapper.fit(x_train, y_train)\n",
        "    selected_features_idx = list(mymodel.k_feature_idx_)\n",
        "\n",
        "    # Get feature names\n",
        "    feature_names = x_train.columns\n",
        "\n",
        "    # Visualizing feature importance\n",
        "    feature_importance = classifier.fit(x_train, y_train).feature_importances_\n",
        "    sorted_idx = feature_importance.argsort()[::-1]\n",
        "    top_k_idx = sorted_idx[:k_features]\n",
        "    top_k_features = [feature_names[idx] for idx in top_k_idx]\n",
        "    top_k_importance = feature_importance[top_k_idx]\n",
        "\n",
        "    plt.figure(figsize=(10, 6))\n",
        "    plt.barh(top_k_features, top_k_importance, color='skyblue')\n",
        "    plt.xlabel('Feature Importance')\n",
        "    plt.title('Top Features Selected by the Classifier')\n",
        "    plt.gca().invert_yaxis()\n",
        "    plt.show()\n",
        "\n",
        "    return selected_features_idx\n"
      ],
      "metadata": {
        "id": "KiO1afiHtOcn"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "SequentialFeatureSelectionWarpper(RandomForestClassifier(), 5, x_train, y_train, forward=True)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 582
        },
        "id": "FwiL_5u227p6",
        "outputId": "e7fa252c-39e6-4eea-8fa6-28e6b4fa8678"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[1, 4, 5, 6, 7]"
            ]
          },
          "metadata": {},
          "execution_count": 58
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "SequentialFeatureSelectionWarpper(DecisionTreeClassifier(), 5, x_train, y_train, forward=True)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 582
        },
        "id": "i1KSTJclONjz",
        "outputId": "e738c12d-0e30-4117-8ad3-092ad0d12913"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[1, 2, 3, 4, 6]"
            ]
          },
          "metadata": {},
          "execution_count": 59
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def SequentialFeatureSelectionWarpper(classifiers, k_features, x_train, y_train, scoring=\"roc_auc\", forward=True):\n",
        "    selected_features_all = []\n",
        "\n",
        "    fig, axs = plt.subplots(1, len(classifiers), figsize=(15, 6))\n",
        "\n",
        "    for i, classifier in enumerate(classifiers):\n",
        "        # Feature selection\n",
        "        model_wrapper = SequentialFeatureSelector(classifier, k_features, scoring=scoring, forward=forward)\n",
        "        mymodel = model_wrapper.fit(x_train, y_train)\n",
        "        selected_features_idx = list(mymodel.k_feature_idx_)\n",
        "        selected_features_all.append(selected_features_idx)\n",
        "\n",
        "        # Get feature names\n",
        "        feature_names = x_train.columns\n",
        "\n",
        "        # Visualizing feature importance\n",
        "        feature_importance = classifier.fit(x_train, y_train).feature_importances_\n",
        "        sorted_idx = feature_importance.argsort()[::-1]\n",
        "        top_k_idx = sorted_idx[:k_features]\n",
        "        top_k_features = [feature_names[idx] for idx in top_k_idx]\n",
        "        top_k_importance = feature_importance[top_k_idx]\n",
        "\n",
        "        axs[i].barh(top_k_features, top_k_importance, color='skyblue')\n",
        "        axs[i].set_xlabel('Feature Importance')\n",
        "        axs[i].set_title(f'{type(classifier).__name__} - Top Features Selected')\n",
        "        axs[i].invert_yaxis()\n",
        "\n",
        "    plt.tight_layout()\n",
        "    plt.show()\n",
        "\n",
        "    return selected_features_all"
      ],
      "metadata": {
        "id": "6lL8_uQ1gACS"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Sequential Forward Feature Selection\n",
        "classifiers = [RandomForestClassifier(), DecisionTreeClassifier()]\n",
        "selected_features = SequentialFeatureSelectionWarpper(classifiers, 5, x_train, y_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 575
        },
        "id": "13Kf2kwUhtW3",
        "outputId": "effa229f-f2c7-4531-97c1-9d1d60f9779a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1500x600 with 2 Axes>"
            ],
            "image/png": 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x8MMPR/fu3eOxxx6Lgw8+OCJ+Py4HDRoUJ554Yuywww4xZ86cePfdd+O9995b6T5r3LhxvPzyy/HKK6/EX/7yl5X2b3XeZyu64oor4qKLLooePXrEiSeeGD/++GPcdNNNsccee8T48eMzJxEjR46Mbt26Rb169eLMM8+MunXrxieffBLPPPNMnHnmmXHKKafEd999V+TXlSMiTjnllBg+fHiccMIJ0a9fv5g6dWrcfPPNMX78+Kx/ny6++OK4/PLLY7/99ov99tsv3nvvvdhnn31i0aJFqxglAEqj4447Lv7+97/HSy+9FCeddFJERLE/Q+fNmxe77757fPLJJ9G7d+9o165d/PTTT/H000/HN998k3VruuUdeuih8dFHH8UZZ5wRTZo0iRkzZsTIkSPjq6++Sv1M/O2332LPPfeMzz//PE4//fRo2rRpPPLII9GrV6+YNWtWnHnmmVnti3NOURyTJk2Ko446Kk455ZQ46aSTYosttoj58+dHx44d49tvv41TTjklGjVqFG+++Wb0798/pk+fnnX/4+J8vr7xxhvx888/x1lnnfWHvzX5yCOPxPz58+PUU0+NmjVrxjvvvBM33XRTfPPNN/HII49k2q1q38+YMSP22WefqF27dlx44YVRvXr1mDZt2kovyjjllFOiQYMG8c9//jP69esXHTp0iDp16qS2L25tE1H0OdG6trrnkP/9739jxIgR0a9fv8jNzY2hQ4dG165d45133oltttlmleubP39+ofq7WrVqUb58+WIfi1988UU8+eSTcfjhh0fTpk3jhx9+iNtuuy06duwYH3/8cdSvX3+l54B/xGWXXRYVKlSIc889NxYuXBgVKlSIV155Jfbdd99o3759DBgwIMqUKRPDhg2Lv/zlL/H6669n/hC0qvPANI0bN46xY8fGhx9+uMp9uzrvsxUVty4ePnx49O7dO7beeuvo379/VK9ePcaPHx8vvPBCHH300fGPf/wjZs+enXWrqIJz0+Ke20f8fj543333xdFHHx277LJLvPLKK7H//vsXZ5hYGxKANTBs2LAkIpKXX345+fHHH5Ovv/46efTRR5PatWsnubm5yddff51pu2TJkmThwoVZ8//yyy9JnTp1kt69e2emTZ06NYmIpGbNmsnPP/+cmf7UU08lEZH85z//yUzbdtttk3r16iWzZs3KTHvppZeSiEgaN26cmfbkk08mEZFcfvnlWes/7LDDkpycnOTzzz/PTIuIJDc3N5k6dWpm2m233ZZERFK3bt1kzpw5men9+/dPIiKrbc+ePZOIKPTo2LFjkiRJMmPGjKRChQrJPvvskyxdujQz380335xERHL33XdnpnXs2DGJiOTWW2/N6ve9996blClTJnn99dezpt96661JRCRjxoxJkiRJhgwZkkRE8uOPPyZpxo0bl0REMmzYsKzpBft7yJAhqfMur2Dcll/O/PnzC7V78MEHk4hIXnvttcy0atWqJX379k1d9vjx45OISB555JGV9qFx48ZJz549C/XpmmuuyWpX8L4tGLe5c+cm1atXT0466aSsdt9//31SrVq1rOkF43vhhReutC9r6pprrsnq44QJE5KISE488cSsdueee24SEckrr7ySmda4ceMkIpLHHnssM2327NlJvXr1ku22226V6y7q/bv82Pbp0yepV69e8tNPP2XNd+SRRybVqlXLjHtxj/kff/wxiYhkwIABhfrSsWPHzLGzvJ49e2Yd4wVjXbVq1WTGjBlZbTt16pS0bt06WbBgQWbasmXLkl122SVp0aJFZlrbtm2T/ffff6X7pigffvhhUrFixSQikm233TY588wzkyeffDL59ddfs9qtzvtswIAByfJl2rRp05KyZcsmV1xxRda8H3zwQVKuXLnM9CVLliRNmzZNGjdunPzyyy9ZbZctW5b5/759+2Ytv8Drr7+eRERy//33Z01/4YUXsqYX/Du2//77Zy3373//exIRWcchAKVDQf0zbty41DbVqlXLqhWK+xl68cUXJxGRPP7444WWWfA5sWKt+MsvvxRZp61oxVrg+uuvTyIiue+++zLTFi1alOy8885JlSpVMrX66pxTLG///ffPqjGS5P/XVi+88ELW9MsuuyypXLly8tlnn2VNv/DCC5OyZcsmX331VZIkxf98veGGG5KISJ544omV7pMCr776ahIRyauvvpqZVlT9PWjQoCQnJyf58ssvkyQp3r5/4oknVvl+SZKkUA1X0KcV6/Y/WtskSfo50dq2Yn20uueQEZG8++67mWlffvllkpeXlxx88MErXW/Be7WoR8HYFvdYXLBgQdY5ZsHyc3Nzk0svvTQzLe0cMEkKn1MVWPFYLBjrzTffPOt9t2zZsqRFixZJly5dsurE+fPnJ02bNk323nvvzLRVnQemeemll5KyZcsmZcuWTXbeeefk/PPPT1588cVk0aJFWe1W53224vlFcY/bWbNmJfn5+cmOO+6Y/Pbbb1ltl9/+ov5tSZLin9sXnA+edtppWe2OPvro1HMp1i63cwHWis6dO0ft2rWjYcOGcdhhh0XlypXj6aefzroivGzZspl7Gi5btix+/vnnWLJkSWy//fZFfl3riCOOyLqSveAv5F988UVEREyfPj0mTJgQPXv2zLoSee+9946tttoqa1nPPfdclC1bNvr165c1/ZxzzokkSeL555/Pmt6pU6esq1923HHHiPj9io3lr8oumF7QpwJ5eXkxcuTIrMfgwYMjIuLll1+ORYsWxVlnnZV1r7iTTjopqlatGs8++2zWsnJzc+OEE07ImvbII4/ElltuGa1atYqffvop8yi4CrbgdhkFf1V/6qmnCn2ldFUKvob3R69Cj8j+IcqCK5sLfmF++TGvXr16vP322/Hdd98VuZyC8X3xxRdj/vz5f7g/aUaOHBmzZs2Ko446Kmt/li1bNnbcccdCtxyKiDj11FPXej9W5rnnnouIiLPPPjtr+jnnnBMRUeh9U79+/cwVKRERVatWjeOPPz7Gjx8f33///SrXd9BBBxV6D3fp0iWSJInHHnssDjjggEiSJGt/denSJWbPnp0Z29U95teGQw89NOsHmH7++ed45ZVXokePHjF37txMX2fOnBldunSJyZMnx7fffhsRv78PP/roo5g8efJqrXPrrbeOCRMmxLHHHhvTpk2LG264Ibp37x516tSJO+64I9Puj7zPCjz++OOxbNmy6NGjR9a8devWjRYtWmTmHT9+fEydOjXOOuusQlfVFPUV6hU98sgjUa1atdh7772z1tO+ffuoUqVKZj0F/46dccYZWcv1Q0kAG7YqVarE3LlzI2L1PkMfe+yxaNu2bVbtUSDt86dixYpRoUKFGD16dJG3Rkjz3HPPRd26deOoo47KTCtfvnz069cv5s2bF//973+z2q/qnKK4mjZtGl26dMma9sgjj8Tuu+8em2yySdbnZufOnWPp0qWZW3kU9/N1bdffv/76a/z000+xyy67RJIkMX78+EybVe37gjrimWeeWSe/gVTc2qZAUedE69rqnkPuvPPO0b59+8zzRo0axUEHHRQvvvhiLF26dJXrO/nkkwvV323btl2tYzE3Nzdzjrl06dKYOXNm5pae66r+7tmzZ9b7bsKECTF58uQ4+uijY+bMmZn+/vrrr9GpU6d47bXXMuemqzoPTLP33nvH2LFj48ADD4yJEyfG1VdfHV26dIkGDRpk3eJmdd9nyyvucTty5MiYO3duXHjhhYV+I6C49Xdxzu0LzgdXfD+qv9cft3MB1op//etf0bJly5g9e3bcfffd8dprrxX6kceIiHvuuScGDx4cn376aVYx1rRp00JtGzVqlPW8oPgtKPS+/PLLiIho0aJFoXlXLBK+/PLLqF+/fqGCtOCX1guWlbbughC3YcOGRU5fsfgsW7ZsdO7cuVC/ll/XFltskTW9QoUKsfnmmxfqS4MGDQr9oNLkyZPjk08+Sf219oIfUzniiCPizjvvjBNPPDEuvPDC6NSpUxxyyCFx2GGHrfLHXqpWrRoRkTmR+iN+/vnnGDhwYDz00EOFfuBl+XuZX3311dGzZ89o2LBhtG/fPvbbb784/vjjY/PNN4+I398fZ599dlx33XVx//33x+677x4HHnhgHHvssUXeymV1FYSmabfiKNgXBcqVK1folkFFWbRoUfz8889Z02rXrv2Hvp775ZdfRpkyZaJ58+ZZ0+vWrRvVq1cv9L5p3rx5oaKtZcuWEfH77Zbq1q270vVtttlmRb6HZ8yYEbNmzYrbb789br/99iLnXX6sV+eYXxtWXO7nn38eSZLERRddFBdddFFqfxs0aBCXXnppHHTQQdGyZcvYZpttomvXrnHccccV69YyLVu2jHvvvTeWLl0aH3/8cTzzzDNx9dVXx8knnxxNmzaNzp07r/b7bHmTJ0+OJEmK/PcuIjJfJS34HYrifGU4bT2zZ88u8l6SEf9/bNP+/a1du3ZWUAHAhmXevHmZz4DV+QydMmVKHHrooau1rtzc3LjqqqvinHPOiTp16sROO+0U3bp1i+OPP36ldcqXX34ZLVq0KFTLFreuX/GcoriKql0mT54c77///ipr8uJ+vq6N+vurr76Kiy++OJ5++ulC21hQfxdn33fs2DEOPfTQGDhwYAwZMiT23HPP6N69exx99NFFnuetruLWNgWKOicqym+//ZZ1nhERq6x706zuOWRR29KyZcuYP39+/Pjjj6vsR4sWLYqsv995551iH4vLli2LG264IYYOHRpTp07NCu9r1qy50vX/USseGwU1b8+ePVPnmT17dmyyySarPA9cmQ4dOsTjjz8eixYtiokTJ8YTTzwRQ4YMicMOOywmTJgQW2211Wq/z1bcjuIct2uj/i7OuX3B+WCzZs2yXl8xV2DdEaIDa8UOO+wQ22+/fUREdO/ePXbbbbc4+uijY9KkSZl7fd13333Rq1ev6N69e5x33nmx6aabRtmyZWPQoEFZP0BaIC1oTFb4EZd1IW3dJdGn5f+qX2DZsmXRunXruO6664qcpyDsr1ixYrz22mvx6quvxrPPPhsvvPBCjBgxIv7yl7/ESy+9tNIwt1WrVhERmR9U/SN69OgRb775Zpx33nmx7bbbRpUqVWLZsmXRtWvXrCvje/ToEbvvvns88cQT8dJLL8U111wTV111VTz++OOx7777RkTE4MGDo1evXvHUU0/FSy+9FP369YtBgwbFW2+9VaxAe2UK+nLvvfcWWdyWK5f9cbn8FR4r8+abb8Zee+2VNW3q1Kkrve/1qhTnaoZ1qWBfHXvssamFcUHovLrHfFFycnKKPL7SruZZ8Xgp6O+5555b6OqxAgV/mNhjjz1iypQpmffYnXfeGUOGDIlbb701TjzxxGL1t2zZstG6deto3bp17LzzzrHXXnvF/fffH507d17t99mK25GTkxPPP/98kcftyu73vzqWLVsWm266adx///1Fvp5W3AOw4fvmm29i9uzZmc/F1fkM/aPOOuusOOCAA+LJJ5+MF198MS666KIYNGhQvPLKK7Hddtut0bILrK36Pa0m33vvveP8888vcp6CCxiK+/m6fP3dvXv31epfxO/10d577x0///xzXHDBBdGqVauoXLlyfPvtt9GrV6+s+ntV+z4nJyceffTReOutt+I///lPvPjii9G7d+8YPHhwvPXWW2tce6xubVPU/i/KiBEjCl2xvj7OH9el1TkW//nPf8ZFF10UvXv3jssuuyxq1KgRZcqUibPOOqvY30xOO99YunRpkWOVVn9fc801hX73rEDB+BbnPHBVKlSoEB06dIgOHTpEy5Yt44QTTohHHnkkBgwYsEY19Pqqi4t7bk/JE6IDa11BSLbXXnvFzTffHBdeeGFERDz66KOx+eabx+OPP571wTxgwIA/tJ7GjRtHRBR564VJkyYVavvyyy/H3Llzs64k+PTTT7OWtT4UrGvSpElZf2FftGhRTJ06NfUK9uU1a9YsJk6cGJ06dVplqFqmTJno1KlTdOrUKa677rr45z//Gf/4xz/i1Vdfjc6dO6fO37Jly9hiiy3iqaeeihtuuGG1C+VffvklRo0aFQMHDoyLL744Mz3tVhn16tWL0047LU477bSYMWNGtGvXLq644oqs4qkgnPy///u/ePPNN2PXXXeNW2+9NS6//PLV6tuKCv6av+mmmxZr/xdX27ZtY+TIkVnT/uiVMI0bN45ly5bF5MmTM1e/RET88MMPMWvWrELv4YKrx5Yf388++ywiYo1C/IJfgl+6dOkq91Vxj/mVvYc32WSTIr9uveKVP2kKjrHy5csXa2xr1KgRJ5xwQpxwwgkxb9682GOPPeKSSy4pdoi+vII/LE6fPj0i1ux91qxZs0iSJJo2bZo5IU9rFxHx4YcfrnQdafu8WbNm8fLLL8euu+660pPV5f/9Xf7fsR9//HG1r+wDoHQo+LHpgpBudT5DmzVrFh9++OEfWm+zZs3inHPOiXPOOScmT54c2267bQwePDjuu+++Its3btw43n///Vi2bFnWRQ0lUdc3a9Ys5s2bV6z9U5zP19122y022WSTePDBB+Pvf//7an978YMPPojPPvss7rnnnjj++OMz01esR5fv16r2/U477RQ77bRTXHHFFfHAAw/EMcccEw899NAfqo1WXHdxapvV1aVLl9TtXV2rew5Z1HnOZ599FpUqVVqjwHV1jsVHH3009tprr7jrrruyps+aNSvrB35XVX/PmjWr0PQvv/yyWFeIF9SjVatWLVbNW5zzwOIqqv7+o++z4h63y9ffK/vD4srq7+Kc2xecD06ZMiXr6vMVsw/WHfdEB9aJPffcM3bYYYe4/vrrY8GCBRHx/68CWf5KgLfffjvGjh37h9ZRr1692HbbbeOee+7J+sreyJEj4+OPP85qu99++8XSpUvj5ptvzpo+ZMiQyMnJ+UMf0H9U586do0KFCnHjjTdm7Yu77rorZs+eXaxf1+7Ro0d8++23WfdbLvDbb7/Fr7/+GhFR6FYiEZG5GmDhwoUREVG5cuWIiCILpYEDB8bMmTPjxBNPjCVLlhR6/aWXXopnnnmmyD4WNd4REddff33W86VLlxb6yuWmm24a9evXz/Rxzpw5hdbfunXrKFOmTKbNmujSpUtUrVo1/vnPfxZ5z8cff/zxDy13k002ic6dO2c9VrxPXnHtt99+EVF4/xVcsbDi++a7776LJ554IvN8zpw58e9//zu23XbbPxzkR/w+roceemg89thjRZ4sL7+vinvMV6pUKSKKfg82a9YsPv3006zlTpw4McaMGVOs/m666aax5557xm233ZYpptP6O3PmzKzXqlSpEs2bN1/le+z1118v8n1TcN/CgiJ3Td5nhxxySJQtWzYGDhxY6JhKkiTT93bt2kXTpk3j+uuvL7Q/l58v7bjv0aNHLF26NC677LJCfViyZEmmfefOnaN8+fJx0003ZS13xfcnABuGV155JS677LJo2rRpHHPMMRGxep+hhx56aOZ2CitKuwp4/vz5mfOEAs2aNYv8/PyVfvbut99+8f3338eIESMy05YsWRI33XRTVKlSJTp27LjyjV2LevToEWPHjo0XX3yx0GuzZs3K1K/F/XytVKlSXHDBBfHJJ5/EBRdcUOS+u+++++Kdd94psj9F1V5JksQNN9yQ1a44+/6XX34ptP4VzyPWRHFrm9VVr169QvX3H7W655Bjx47NuqXo119/HU899VTss88+f+h2jgVW51gsW7Zsof35yCOPZO6ZXmBl54DNmjWLt956KxYtWpSZ9swzz8TXX39drP62b98+mjVrFtdee23Mmzcvtb/FOQ9M8+qrrxZ5fKxYf6/J+6y4x+0+++wT+fn5MWjQoELH1Yr194rbW7Ce4pzbF7zfbrzxxqw26u/1x5XowDpz3nnnxeGHHx7Dhw+Pv/71r9GtW7d4/PHH4+CDD479998/pk6dGrfeemtstdVWRX64FsegQYNi//33j9122y169+4dP//8c9x0002x9dZbZy3zgAMOiL322iv+8Y9/xLRp06Jt27bx0ksvxVNPPRVnnXVWofuKrUu1a9eO/v37x8CBA6Nr165x4IEHxqRJk2Lo0KHRoUOHOPbYY1e5jOOOOy4efvjh+Otf/xqvvvpq7LrrrrF06dL49NNP4+GHH44XX3wxtt9++7j00kvjtddei/333z8aN24cM2bMiKFDh8Zmm20Wu+22W0T8XiRVr149br311sjPz4/KlSvHjjvuGE2bNo0jjjgiPvjgg7jiiiti/PjxcdRRR0Xjxo1j5syZ8cILL8SoUaPigQceKLKPVatWjT322COuvvrqWLx4cTRo0CBeeumlmDp1ala7uXPnxmabbRaHHXZYtG3bNqpUqRIvv/xyjBs3LvNjrK+88kqcfvrpcfjhh0fLli1jyZIlce+992YC3TVVtWrVuOWWW+K4446Ldu3axZFHHhm1a9eOr776Kp599tnYddddCxXP61vbtm2jZ8+ecfvtt8esWbOiY8eO8c4778Q999wT3bt3L3TbmJYtW0afPn1i3LhxUadOnbj77rvjhx9+iGHDhq1xX6688sp49dVXY8cdd4yTTjopttpqq/j555/jvffei5dffjnzx5viHvMVK1aMrbbaKkaMGBEtW7aMGjVqxDbbbBPbbLNN9O7dO6677rro0qVL9OnTJ2bMmBG33nprbL311pkf31qVf/3rX7HbbrtF69at46STTorNN988fvjhhxg7dmx88803MXHixIiI2GqrrWLPPfeM9u3bR40aNeLdd9+NRx99NE4//fSVLv+qq66K//3vf3HIIYdkbmXz3nvvxb///e+oUaNG5sd+1uR91qxZs7j88sujf//+MW3atOjevXvk5+fH1KlT44knnoiTTz45zj333ChTpkzccsstccABB8S2224bJ5xwQtSrVy8+/fTT+OijjzIn+QU/etWvX7/o0qVLlC1bNo488sjo2LFjnHLKKTFo0KCYMGFC7LPPPlG+fPmYPHlyPPLII3HDDTfEYYcdFrVr145zzz03Bg0aFN26dYv99tsvxo8fH88//3zWVU4AlD7PP/98fPrpp7FkyZL44Ycf4pVXXomRI0dG48aN4+mnn876g39xP0PPO++8ePTRR+Pwww+P3r17R/v27ePnn3+Op59+Om699dZo27ZtoX589tln0alTp+jRo0dstdVWUa5cuXjiiSfihx9+iCOPPDK1/yeffHLcdttt0atXr/jf//4XTZo0iUcffTTGjBkT119//Rr9KOfqOu+88+Lpp5+Obt26Ra9evaJ9+/bx66+/xgcffBCPPvpoTJs2LWrVqlXsz9eCZX700UcxePDgePXVV+Owww6LunXrxvfffx9PPvlkvPPOO/Hmm28W2Z9WrVpFs2bN4txzz41vv/02qlatGo899lihb4kVZ9/fc889MXTo0Dj44IOjWbNmMXfu3LjjjjuiatWqmYs71kRxa5uStLrnkNtss0106dIl+vXrF7m5uTF06NCI+P3CpDVV3GOxW7ducemll8YJJ5wQu+yyS3zwwQdx//33F7qCfGXngCeeeGI8+uij0bVr1+jRo0dMmTIl7rvvvmKfM5cpUybuvPPO2HfffWPrrbeOE044IRo0aBDffvttvPrqq1G1atX4z3/+U6zzwDRnnHFGzJ8/Pw4++OBo1apVLFq0KN58880YMWJENGnSJHNLnzV5nxX3uK1atWoMGTIkTjzxxOjQoUMcffTRsckmm8TEiRNj/vz5cc8990TE7/X3iBEj4uyzz44OHTpElSpV4oADDij2uf22224bRx11VAwdOjRmz54du+yyS4waNSo+//zzYo0La0ECsAaGDRuWREQybty4Qq8tXbo0adasWdKsWbNkyZIlybJly5J//vOfSePGjZPc3Nxku+22S5555pmkZ8+eSePGjTPzTZ06NYmI5Jprrim0zIhIBgwYkDXtscceS7bccsskNzc32WqrrZLHH3+80DKTJEnmzp2b/O1vf0vq16+flC9fPmnRokVyzTXXJMuWLSu0jr59+2ZNS+vTq6++mkRE8sgjj2Sm9ezZM6lcufLKdluSJEly8803J61atUrKly+f1KlTJzn11FOTX375JatNx44dk6233rrI+RctWpRcddVVydZbb53k5uYmm2yySdK+fftk4MCByezZs5MkSZJRo0YlBx10UFK/fv2kQoUKSf369ZOjjjoq+eyzz7KW9dRTTyVbbbVVUq5cuSQikmHDhmW9XrCcTTfdNClXrlxSu3bt5IADDkieeuqpQvto+Xm/+eab5OCDD06qV6+eVKtWLTn88MOT7777LmscFy5cmJx33nlJ27Ztk/z8/KRy5cpJ27Ztk6FDh2aW88UXXyS9e/dOmjVrluTl5SU1atRI9tprr+Tll1/O6mfjxo2Tnj17FurTiuNW8L6dOnVq1vRXX3016dKlS1KtWrUkLy8vadasWdKrV6/k3XffzbQp7viuqWuuuaZQHxcvXpwMHDgwadq0aVK+fPmkYcOGSf/+/ZMFCxZkzdu4ceNk//33T1588cWkTZs2SW5ubtKqVaus9+nKFHUMrOiHH35I+vbtmzRs2DApX758Urdu3aRTp07J7bffnmlT3GM+SZLkzTffTNq3b59UqFCh0HF+3333JZtvvnlSoUKFZNttt01efPHF1fp3I0mSZMqUKcnxxx+f1K1bNylfvnzSoEGDpFu3bsmjjz6aaXP55ZcnO+ywQ1K9evWkYsWKSatWrZIrrrgiWbRo0Ur3xZgxY5K+ffsm22yzTVKtWrWkfPnySaNGjZJevXolU6ZMKdS+OO+zAQMGJEWVaY899liy2267JZUrV04qV66ctGrVKunbt28yadKkrHZvvPFGsvfee2eOqTZt2iQ33XRT5vUlS5YkZ5xxRlK7du0kJyen0Lpuv/32pH379knFihWT/Pz8pHXr1sn555+ffPfdd5k2S5cuTQYOHJjUq1cvqVixYrLnnnsmH374YaHjEIDSoaD+KXhUqFAhqVu3brL33nsnN9xwQzJnzpwi5yvOZ2iSJMnMmTOT008/PWnQoEFSoUKFZLPNNkt69uyZ/PTTT0mSFK4Vf/rpp6Rv375Jq1atksqVKyfVqlVLdtxxx+Thhx/OWm7Hjh2Tjh07Zk374YcfkhNOOCGpVatWUqFChaR169aF6tfVPacosP/++xeqUwpqq6LMnTs36d+/f9K8efOkQoUKSa1atZJddtklufbaawvVEMX5fC3w6KOPJvvss09So0aNpFy5ckm9evWSI444Ihk9enSmTcG5yKuvvpqZ9vHHHyedO3dOqlSpktSqVSs56aSTkokTJ672vn/vvfeSo446KmnUqFGSm5ubbLrppkm3bt2y6pWi9mVR50dJsma1zcrOidamvn37Furj6p5D3nfffUmLFi0yte/yY5NmVXVsgeIciwsWLEjOOeecTH226667JmPHji3yOFrZOeDgwYOTBg0aJLm5ucmuu+6avPvuu4WWkTbWBcaPH58ccsghSc2aNZPc3NykcePGSY8ePZJRo0YlSVK888A0zz//fNK7d++kVatWSZUqVZIKFSokzZs3T84444zkhx9+KNS+OO+zos5RkqT4x+3TTz+d7LLLLknFihWTqlWrJjvssEPy4IMPZl6fN29ecvTRRyfVq1dPIiJrXcU5t0+SJPntt9+Sfv36JTVr1kwqV66cHHDAAcnXX3+90n/TWHtykmQD/4UFACBLkyZNYptttkm91Q4AALD25OTkRN++fUv826vAuuOe6AAAAAAAkEKIDgAAAAAAKYToAAAAAACQwj3RAQAAAAAghSvRAQAAAAAghRAdAAAAAABSlCvpDkBptmzZsvjuu+8iPz8/cnJySro7AMCfQJIkMXfu3Khfv36UKeOaFigOdTkAsC4UtzYXosNKfPfdd9GwYcOS7gYA8Cf09ddfx2abbVbS3YANgrocAFiXVlWbC9FhJfLz8yPi9wOpatWqJdwbAODPYM6cOdGwYcNMnQGsmrocAFgXilubC9FhJQq+Klq1alXFOgCwVrklBRSfuhwAWJdWVZu7CSMAAAAAAKQQogMAAAAAQAohOgAAAAAApBCiAwAAAABACiE6AAAAAACkEKIDAAAAAEAKIToAAAAAAKQQogMAAAAAQAohOgAAAAAApBCiAwAAAABACiE6AAAAAACkEKIDAAAAAEAKIToAAAAAAKQQogMAAAAAQAohOgAAAAAApBCiAwAAAABACiE6AAAAAACkEKIDAAAAAEAKIToAAAAAAKQQogMAAAAAQIpyJd0B2BBcN3Fm5FVZVNLdAADWgwu3q1XSXQBSqMsBYONRmupyV6IDAAAAAEAKIToAAAAAAKQQogMAAAAAQAohOgAAAAAApBCiAwAAAABACiE6AAAAAACkEKIDAAAAAEAKIToAAAAAAKQQogMAAAAAQAohOgAAAAAApBCiAwAAAABACiE6AAAAAACkEKIDAAAAAEAKIToAAAAAAKQQogMAAAAAQAohOgAAAAAApBCiAwAAAABACiE6AAAAAACkEKIDAAAAAEAKIToAAAAAAKQQogMAAAAAQAohOgAAAAAApBCiAwAAAABACiE6AAAAAACkEKIDAAAAAEAKIToAAAAAAKQQogMAAAAAQAohOgAAAAAApBCiAwAAAABACiE6AAAAAACkEKIDAAAAAEAKIToAAAAAAKQQorNW5eTkxJNPPlnS3QAAgI2auhwAYO0RolNs33//fZx55pnRvHnzyMvLizp16sSuu+4at9xyS8yfP7+kuwcAABsFdTkAwPpVrqQ7wIbhiy++iF133TWqV68e//znP6N169aRm5sbH3zwQdx+++3RoEGDOPDAA0u6mwAA8KemLgcAWP9ciU6xnHbaaVGuXLl49913o0ePHrHlllvG5ptvHgcddFA8++yzccABBxSaZ/To0ZGTkxOzZs3KTJswYULk5OTEtGnTMtPGjBkTe+65Z1SqVCk22WST6NKlS/zyyy8REbFw4cLo169fbLrpppGXlxe77bZbjBs3LjPvL7/8Esccc0zUrl07KlasGC1atIhhw4ZlXv/666+jR48eUb169ahRo0YcdNBBWesGAIANibocAGD9E6KzSjNnzoyXXnop+vbtG5UrVy6yTU5Ozh9a9oQJE6JTp06x1VZbxdixY+ONN96IAw44IJYuXRoREeeff3489thjcc8998R7770XzZs3jy5dusTPP/8cEREXXXRRfPzxx/H888/HJ598ErfcckvUqlUrIiIWL14cXbp0ifz8/Hj99ddjzJgxUaVKlejatWssWrSoyP4sXLgw5syZk/UAAIDSQF0OAFAy3M6FVfr8888jSZLYYostsqbXqlUrFixYEBERffv2jauuumq1l3311VfH9ttvH0OHDs1M23rrrSMi4tdff41bbrklhg8fHvvuu29ERNxxxx0xcuTIuOuuu+K8886Lr776KrbbbrvYfvvtIyKiSZMmmeWMGDEili1bFnfeeWfmZGLYsGFRvXr1GD16dOyzzz6F+jNo0KAYOHDgam8HAACsa+pyAICS4Up0/rB33nknJkyYEFtvvXUsXLjwDy2j4IqXokyZMiUWL14cu+66a2Za+fLlY4cddohPPvkkIiJOPfXUeOihh2LbbbeN888/P958881M24kTJ8bnn38e+fn5UaVKlahSpUrUqFEjFixYEFOmTClynf3794/Zs2dnHl9//fUf2i4AAFhf1OUAAOuWK9FZpebNm0dOTk5MmjQpa/rmm28eEREVK1Yscr4yZX7/G02SJJlpixcvzmqTNm9x7bvvvvHll1/Gc889FyNHjoxOnTpF375949prr4158+ZF+/bt4/777y80X+3atYtcXm5ubuTm5q5RnwAAYF1QlwMAlAxXorNKNWvWjL333jtuvvnm+PXXX4s9X0FBPH369My0CRMmZLVp06ZNjBo1qsj5mzVrFhUqVIgxY8Zkpi1evDjGjRsXW221VdZ6evbsGffdd19cf/31cfvtt0dERLt27WLy5Mmx6aabRvPmzbMe1apVK/Z2AABAaaAuBwAoGUJ0imXo0KGxZMmS2H777WPEiBHxySefxKRJk+K+++6LTz/9NMqWLVtonubNm0fDhg3jkksuicmTJ8ezzz4bgwcPzmrTv3//GDduXJx22mnx/vvvx6effhq33HJL/PTTT1G5cuU49dRT47zzzosXXnghPv744zjppJNi/vz50adPn4iIuPjii+Opp56Kzz//PD766KN45plnYsstt4yIiGOOOSZq1aoVBx10ULz++usxderUGD16dPTr1y+++eabdb/TAABgLVOXAwCsf0J0iqVZs2Yxfvz46Ny5c/Tv3z/atm0b22+/fdx0001x7rnnxmWXXVZonvLly8eDDz4Yn376abRp0yauuuqquPzyy7PatGzZMl566aWYOHFi7LDDDrHzzjvHU089FeXK/X6noSuvvDIOPfTQOO6446Jdu3bx+eefx4svvhibbLJJRERUqFAh+vfvH23atIk99tgjypYtGw899FBERFSqVClee+21aNSoURxyyCGx5ZZbRp8+fWLBggVRtWrVdbzHAABg7VOXAwCsfznJ8jfGA7LMmTMnqlWrFgNe+yLyquSXdHcAgPXgwu1qrdPlF9QXs2fPFiBCManLAWDjs67r8oji1+auRAcAAAAAgBRCdAAAAAAASCFEBwAAAACAFEJ0AAAAAABIIUQHAAAAAIAUQnQAAAAAAEghRAcAAAAAgBRCdAAAAAAASCFEBwAAAACAFEJ0AAAAAABIIUQHAAAAAIAUQnQAAAAAAEghRAcAAAAAgBRCdAAAAAAASCFEBwAAAACAFEJ0AAAAAABIIUQHAAAAAIAUQnQAAAAAAEghRAcAAAAAgBRCdAAAAAAASCFEBwAAAACAFEJ0AAAAAABIIUQHAAAAAIAUQnQAAAAAAEghRAcAAAAAgBRCdAAAAAAASCFEBwAAAACAFEJ0AAAAAABIIUQHAAAAAIAUQnQAAAAAAEghRAcAAAAAgBRCdAAAAAAASFGupDsAG4Kz29aMqlWrlnQ3AABgo6YuBwBKgivRAQAAAAAghRAdAAAAAABSCNEBAAAAACCFEB0AAAAAAFII0QEAAAAAIIUQHQAAAAAAUgjRAQAAAAAghRAdAAAAAABSCNEBAAAAACCFEB0AAAAAAFII0QEAAAAAIIUQHQAAAAAAUgjRAQAAAAAghRAdAAAAAABSCNEBAAAAACCFEB0AAAAAAFII0QEAAAAAIIUQHQAAAAAAUpQr6Q7AhuC6iTMjr8qiku4GABugC7erVdJdAPjTUJcDUFqo8zcurkQHAAAAAIAUQnQAAAAAAEghRAcAAAAAgBRCdAAAAAAASCFEBwAAAACAFEJ0AAAAAABIIUQHAAAAAIAUQnQAAAAAAEghRAcAAAAAgBRCdAAAAAAASCFEBwAAAACAFEJ0AAAAAABIIUQHAAAAAIAUQnQAAAAAAEghRAcAAAAAgBRCdAAAAAAASCFEBwAAAACAFEJ0AAAAAABIIUQHAAAAAIAUQnQAAAAAAEghRAcAAAAAgBRCdAAAAAAASCFEBwAAAACAFEJ0AAAAAABIIUQHAAAAAIAUQnQAAAAAAEghRAcAAAAAgBRCdAAAAAAASCFEBwAAAACAFEJ0AAAAAABIIUQHAAAAAIAUQnQAAAAAAEghRKfU6tWrV+Tk5GQeNWvWjK5du8b777+faVPw2ltvvZU178KFC6NmzZqRk5MTo0ePzmr/5JNPrqctAACAPwe1OQCwMROiU6p17do1pk+fHtOnT49Ro0ZFuXLlolu3blltGjZsGMOGDcua9sQTT0SVKlXWZ1cBAOBPTW0OAGyshOiUarm5uVG3bt2oW7dubLvttnHhhRfG119/HT/++GOmTc+ePeOhhx6K3377LTPt7rvvjp49e5ZElwEA4E9JbQ4AbKyE6Gww5s2bF/fdd180b948atasmZnevn37aNKkSTz22GMREfHVV1/Fa6+9Fscdd1xJdRUAAP7U1OYAwMakXEl3AFbmmWeeyXz189dff4169erFM888E2XKZP/9p3fv3nH33XfHscceG8OHD4/99tsvateuvdrrW7hwYSxcuDDzfM6cOWu2AQAA8CexPmtzdTkAUJq4Ep1Sba+99ooJEybEhAkT4p133okuXbrEvvvuG19++WVWu2OPPTbGjh0bX3zxRQwfPjx69+79h9Y3aNCgqFatWubRsGHDtbEZAACwwVuftbm6HAAoTYTolGqVK1eO5s2bR/PmzaNDhw5x5513xq+//hp33HFHVruaNWtGt27dok+fPrFgwYLYd999/9D6+vfvH7Nnz848vv7667WxGQAAsMFbn7W5uhwAKE3czoUNSk5OTpQpUybrh4oK9O7dO/bbb7+44IILomzZsn9o+bm5uZGbm7um3QQAgD+9dVmbq8sBgNJEiE6ptnDhwvj+++8jIuKXX36Jm2++OebNmxcHHHBAobZdu3aNH3/8MapWrbq+uwkAAH96anMAYGMlRKdUe+GFF6JevXoREZGfnx+tWrWKRx55JPbcc89CbXNycqJWrVrruYcAALBxUJsDABsrITql1vDhw2P48OErbZMkSepr1atXL/T6ytoDAABFU5sDABszPywKAAAAAAAphOgAAAAAAJBCiA4AAAAAACmE6AAAAAAAkEKIDgAAAAAAKYToAAAAAACQQogOAAAAAAAphOgAAAAAAJBCiA4AAAAAACmE6AAAAAAAkEKIDgAAAAAAKYToAAAAAACQQogOAAAAAAAphOgAAAAAAJBCiA4AAAAAACmE6AAAAAAAkEKIDgAAAAAAKYToAAAAAACQQogOAAAAAAAphOgAAAAAAJBCiA4AAAAAACmE6AAAAAAAkEKIDgAAAAAAKYToAAAAAACQQogOAAAAAAAphOgAAAAAAJBCiA4AAAAAACmE6AAAAAAAkEKIDgAAAAAAKYToAAAAAACQQogOAAAAAAAphOgAAAAAAJCiXEl3ADYEZ7etGVWrVi3pbgAAwEZNXQ4AlARXogMAAAAAQAohOgAAAAAApBCiAwAAAABACiE6AAAAAACkEKIDAAAAAEAKIToAAAAAAKQQogMAAAAAQAohOgAAAAAApBCiAwAAAABACiE6AAAAAACkEKIDAAAAAEAKIToAAAAAAKQQogMAAAAAQAohOgAAAAAApBCiAwAAAABACiE6AAAAAACkEKIDAAAAAEAKIToAAAAAAKQoV9IdgA3BdRNnRl6VRSXdDWAjcuF2tUq6CwBQ6qjLgT875wFQOrkSHQAAAAAAUgjRAQAAAAAghRAdAAAAAABSCNEBAAAAACCFEB0AAAAAAFII0QEAAAAAIIUQHQAAAAAAUgjRAQAAAAAghRAdAAAAAABSCNEBAAAAACCFEB0AAAAAAFII0QEAAAAAIIUQHQAAAAAAUgjRAQAAAAAghRAdAAAAAABSCNEBAAAAACCFEB0AAAAAAFII0QEAAAAAIIUQHQAAAAAAUgjRAQAAAAAghRAdAAAAAABSCNEBAAAAACCFEB0AAAAAAFII0QEAAAAAIIUQHQAAAAAAUgjRAQAAAAAghRAdAAAAAABSCNEBAAAAACCFEB0AAAAAAFII0QEAAAAAIIUQHQAAAAAAUgjRAQAAAAAgxToJ0XNycuLJJ58sdvtLLrkktt1223XRlVKpV69e0b1798zzPffcM84666wS68+GYMV9BgBA8ajNV05tvvrU5gDAxma1QvRevXpFTk5O5OTkRPny5aNOnTqx9957x9133x3Lli3LtJs+fXrsu+++a72zKzNt2rTIycmJCRMmrNXlNmnSJLPNlStXjnbt2sUjjzyyVtfx+OOPx2WXXbZWl/lHDR8+PLO9yz/uvPPO9bL+tHG84YYbYvjw4eulDwAAGwK1udp8XVObAwD8brWvRO/atWtMnz49pk2bFs8//3zstddeceaZZ0a3bt1iyZIlERFRt27dyM3NXeudLSmXXnppTJ8+PcaPHx8dOnSII444It588821tvwaNWpEfn7+Gi1j8eLFa6k3EVWrVo3p06dnPY455pi1tvw/olq1alG9evUS7QMAQGmjNleblwS1OQCwsVntED03Nzfq1q0bDRo0iHbt2sXf//73eOqpp+L555/PXI2w4ldGL7jggmjZsmVUqlQpNt9887jooouKLCxvu+22aNiwYVSqVCl69OgRs2fPznr9zjvvjC233DLy8vKiVatWMXTo0MxrTZs2jYiI7bbbLnJycmLPPfcs1nyLFi2K008/PerVqxd5eXnRuHHjGDRoUNZ68/Pzo27dutGyZcv417/+FRUrVoz//Oc/ERHx9ddfR48ePaJ69epRo0aNOOigg2LatGmZeZcuXRpnn312VK9ePWrWrBnnn39+JEmStfwVvzI6ffr02H///aNixYrRtGnTeOCBB6JJkyZx/fXXZ9rk5OTELbfcEgceeGBUrlw5rrjiioiIeOqpp6Jdu3aRl5cXm2++eQwcODBzAhURMWvWrDjxxBOjdu3aUbVq1fjLX/4SEydOzOpPTk5O1K1bN+tRsWLFGD58eKFi+cknn4ycnJzM84Kv/957773RpEmTqFatWhx55JExd+7cTJtly5bF1VdfHc2bN4/c3Nxo1KhRpv9p47jiV0YXLlwY/fr1i0033TTy8vJit912i3HjxmVeHz16dOTk5MSoUaNi++23j0qVKsUuu+wSkyZNCgCAPwu1udp8eWpzAIB1Y63cE/0vf/lLtG3bNh5//PEiX8/Pz4/hw4fHxx9/HDfccEPccccdMWTIkKw2n3/+eTz88MPxn//8J1544YUYP358nHbaaZnX77///rj44ovjiiuuiE8++ST++c9/xkUXXRT33HNPRES88847ERHx8ssvx/Tp0zN9WdV8N954Yzz99NPx8MMPx6RJk+L++++PJk2apG5ruXLlonz58rFo0aJYvHhxdOnSJfLz8+P111+PMWPGRJUqVaJr166xaNGiiIgYPHhwDB8+PO6+++5444034ueff44nnnhipfvz+OOPj++++y5Gjx4djz32WNx+++0xY8aMQu0uueSSOPjgg+ODDz6I3r17x+uvvx7HH398nHnmmfHxxx/HbbfdFsOHD88UwRERhx9+eMyYMSOef/75+N///hft2rWLTp06xc8//7zSPq2OKVOmxJNPPhnPPPNMPPPMM/Hf//43rrzyyszr/fv3jyuvvDIuuuii+Pjjj+OBBx6IOnXqRET6OK7o/PPPj8ceeyzuueeeeO+996J58+bRpUuXQtvxj3/8IwYPHhzvvvtulCtXLnr37r3WthMAoDRSm6vNl6c2BwBYc+XW1oJatWoV77//fpGv/d///V/m/5s0aRLnnntuPPTQQ3H++ednpi9YsCD+/e9/R4MGDSIi4qabbor9998/Bg8eHHXr1o0BAwbE4MGD45BDDomI36+KKChGe/bsGbVr146IiJo1a0bdunUzy13VfF999VW0aNEidtttt8jJyYnGjRunbuOiRYti8ODBMXv27PjLX/4SI0aMiGXLlsWdd96ZueJj2LBhUb169Rg9enTss88+cf3110f//v0z67/11lvjxRdfTF3Hp59+Gi+//HKMGzcutt9++4j4/WqdFi1aFGp79NFHxwknnJB53rt377jwwgujZ8+eERGx+eabx2WXXRbnn39+DBgwIN5444145513YsaMGZmv9F577bXx5JNPxqOPPhonn3xyRETMnj07qlSpkllulSpV4vvvv0/t84qWLVsWw4cPz3wN9rjjjotRo0bFFVdcEXPnzo0bbrghbr755kw/mzVrFrvttltEROo4Lu/XX3+NW265JYYPH565v+cdd9wRI0eOjLvuuivOO++8TNsrrrgiOnbsGBERF154Yey///6xYMGCyMvLK3LZCxcujIULF2aez5kzp9jbDQBQWqjN1eYFNtTaXF0OAJQmay1ET5Ik66uDyxsxYkTceOONMWXKlJg3b14sWbIkqlatmtWmUaNGmSI9ImLnnXeOZcuWxaRJkyI/Pz+mTJkSffr0iZNOOinTZsmSJVGtWrXUPv3666+rnK9Xr16x9957xxZbbBFdu3aNbt26xT777JO1nAsuuCD+7//+LxYsWBBVqlSJK6+8Mvbff/8477zz4vPPPy90z8QFCxbElClTYvbs2TF9+vTYcccdM6+VK1cutt9++0JfGy0wadKkKFeuXLRr1y4zrXnz5rHJJpsUaltQyBeYOHFijBkzJuvqlqVLl8aCBQti/vz5MXHixJg3b17UrFkza77ffvstpkyZknmen58f7733XuZ5mTKr94WFJk2aZO2TevXqZa7W+eSTT2LhwoXRqVOn1Vrm8qZMmRKLFy+OXXfdNTOtfPnyscMOO8Qnn3yS1bZNmzZZ/YiImDFjRjRq1KjIZQ8aNCgGDhz4h/sGAFAaqM3/P7X5hlmbq8sBgNJkrYXon3zySeaeecsbO3ZsHHPMMTFw4MDo0qVLVKtWLR566KEYPHhwsZc9b968iPj9iobli96IiLJly67RfO3atYupU6fG888/Hy+//HL06NEjOnfuHI8++mim7XnnnRe9evWKKlWqRJ06dTInJPPmzYv27dvH/fffX2jdBVdtrEuVK1fOej5v3rwYOHBg5sqa5eXl5cW8efOiXr16MXr06EKvL38/xTJlykTz5s0LtSlTpkyhE4yi7p9Zvnz5rOc5OTmxbNmyiIioWLFi6vasC8v3pWDcCvpSlP79+8fZZ5+deT5nzpxo2LDhuusgAMA6oDbPpjb//zaU2lxdDgCUJmslRH/llVfigw8+iL/97W+FXnvzzTejcePG8Y9//CMz7csvvyzU7quvvorvvvsu6tevHxERb731VpQpUya22GKLqFOnTtSvXz+++OKL1F+ir1ChQkT8fnVHgeLMF/H7L94fccQRccQRR8Rhhx0WXbt2jZ9//jlq1KgRERG1atUqsnBt165djBgxIjbddNNCV+8UqFevXrz99tuxxx57RMTvV9oU3O+wKFtssUUsWbIkxo8fH+3bt4+I3+9J+csvv6T2f/n+TJo0qci+Frz+/fffR7ly5VZ6b8k0tWvXjrlz58avv/6aOUmYMGHCai2jRYsWUbFixRg1alSceOKJhV4vahxX1KxZs6hQoUKMGTMm8xXfxYsXx7hx47J+BOqPyM3NzXydFgBgQ6Q2V5sXV2muzdXlAEBpstoh+sKFC+P777+PpUuXxg8//BAvvPBCDBo0KLp16xbHH398ofYtWrSIr776Kh566KHo0KFDPPvss0X+eE9eXl707Nkzrr322pgzZ07069cvevTokbn33sCBA6Nfv35RrVq16Nq1ayxcuDDefffd+OWXX+Lss8+OTTfdNCpWrBgvvPBCbLbZZpGXlxfVqlVb5XzXXXdd1KtXL7bbbrsoU6ZMPPLII1G3bt1Cv3RflGOOOSauueaaOOigg+LSSy+NzTbbLL788st4/PHH4/zzz4/NNtsszjzzzLjyyiujRYsW0apVq7juuuti1qxZqcts1apVdO7cOU4++eS45ZZbonz58nHOOedExYoVU7+SW+Diiy+Obt26RaNGjeKwww6LMmXKxMSJE+PDDz+Myy+/PDp37hw777xzdO/ePa6++upo2bJlfPfdd/Hss8/GwQcfXOgrqCvacccdo1KlSvH3v/89+vXrF2+//XYMHz58lftpeXl5eXHBBRfE+eefHxUqVIhdd901fvzxx/joo4+iT58+qeO4vMqVK8epp54a5513XtSoUSMaNWoUV199dcyfPz/69OmzWv0BANiQqc3/P7W52hwAYF1ZvRvqRcQLL7wQ9erViyZNmkTXrl3j1VdfjRtvvDGeeuqpIr++eeCBB8bf/va3OP3002PbbbeNN998My666KJC7Zo3bx6HHHJI7LfffrHPPvtEmzZtYujQoZnXTzzxxLjzzjtj2LBh0bp16+jYsWMMHz488zXVcuXKxY033hi33XZb1K9fPw466KBizZefnx9XX311bL/99tGhQ4eYNm1aPPfcc8W612ClSpXitddei0aNGsUhhxwSW265ZfTp0ycWLFiQufrlnHPOieOOOy569uwZO++8c+Tn58fBBx+80uX++9//jjp16sQee+wRBx98cJx00kmRn5+f+mOYBbp06RLPPPNMvPTSS9GhQ4fYaaedYsiQIZkrQnJycuK5556LPfbYI0444YRo2bJlHHnkkfHll19GnTp1Vrm9NWrUiPvuuy+ee+65aN26dTz44INxySWXrHK+FV100UVxzjnnxMUXXxxbbrllHHHEEZn7MqaN44quvPLKOPTQQ+O4446Ldu3axeeffx4vvvhikfenBAD4s1Kb/39qc7U5AMC6kpOk/YoOpcY333wTDRs2jJdffnmNfvSH1TdnzpyoVq1aDHjti8irkr/qGQDWkgu3q1XSXQDWkYL6Yvbs2am3HaH0UpuXDHU5sLFwHgDrV3Fr87X2w6KsPa+88krMmzcvWrduHdOnT4/zzz8/mjRpkrl3IwAAsH6ozQEAEKKXQosXL46///3v8cUXX0R+fn7ssssucf/992f9mj0AALDuqc0BABCil0JdunSJLl26lHQ3AABgo6c2BwBgtX9YFAAAAAAANhZCdAAAAAAASCFEBwAAAACAFEJ0AAAAAABIIUQHAAAAAIAUQnQAAAAAAEghRAcAAAAAgBRCdAAAAAAASCFEBwAAAACAFEJ0AAAAAABIIUQHAAAAAIAUQnQAAAAAAEghRAcAAAAAgBRCdAAAAAAASCFEBwAAAACAFEJ0AAAAAABIIUQHAAAAAIAUQnQAAAAAAEghRAcAAAAAgBRCdAAAAAAASCFEBwAAAACAFEJ0AAAAAABIIUQHAAAAAIAUQnQAAAAAAEghRAcAAAAAgBRCdAAAAAAASCFEBwAAAACAFEJ0AAAAAABIIUQHAAAAAIAU5Uq6A7AhOLttzahatWpJdwMAADZq6nIAoCS4Eh0AAAAAAFII0QEAAAAAIIUQHQAAAAAAUgjRAQAAAAAghRAdAAAAAABSCNEBAAAAACCFEB0AAAAAAFII0QEAAAAAIIUQHQAAAAAAUgjRAQAAAAAghRAdAAAAAABSCNEBAAAAACCFEB0AAAAAAFII0QEAAAAAIIUQHQAAAAAAUgjRAQAAAAAghRAdAAAAAABSCNEBAAAAACBFuZLuAGwIrps4M/KqLCrpbgAbuAu3q1XSXQCADZq6HP4c1MXAhsaV6AAAAAAAkEKIDgAAAAAAKYToAAAAAACQQogOAAAAAAAphOgAAAAAAJBCiA4AAAAAACmE6AAAAAAAkEKIDgAAAAAAKYToAAAAAACQQogOAAAAAAAphOgAAAAAAJBCiA4AAAAAACmE6AAAAAAAkEKIDgAAAAAAKYToAAAAAACQQogOAAAAAAAphOgAAAAAAJBCiA4AAAAAACmE6AAAAAAAkEKIDgAAAAAAKYToAAAAAACQQogOAAAAAAAphOgAAAAAAJBCiA4AAAAAACmE6AAAAAAAkEKIDgAAAAAAKYToAAAAAACQQogOAAAAAAAphOgAAAAAAJBCiA4AAAAAACmE6AAAAAAAkEKIDgAAAAAAKYToAAAAAACQQohOqTV27NgoW7Zs7L///iXdFQAA2KipzQGAjZkQnVLrrrvuijPOOCNee+21+O6770q6OwAAsNFSmwMAGzMhOqXSvHnzYsSIEXHqqafG/vvvH8OHD896/emnn44WLVpEXl5e7LXXXnHPPfdETk5OzJo1K9PmjTfeiN133z0qVqwYDRs2jH79+sWvv/66fjcEAAA2cGpzAGBjJ0SnVHr44YejVatWscUWW8Sxxx4bd999dyRJEhERU6dOjcMOOyy6d+8eEydOjFNOOSX+8Y9/ZM0/ZcqU6Nq1axx66KHx/vvvx4gRI+KNN96I008/faXrXbhwYcyZMyfrAQAAG7OSqM3V5QBAaSJEp1S666674thjj42IiK5du8bs2bPjv//9b0RE3HbbbbHFFlvENddcE1tssUUceeSR0atXr6z5Bw0aFMccc0ycddZZ0aJFi9hll13ixhtvjH//+9+xYMGC1PUOGjQoqlWrlnk0bNhwnW0jAABsCEqiNleXAwCliRCdUmfSpEnxzjvvxFFHHRUREeXKlYsjjjgi7rrrrszrHTp0yJpnhx12yHo+ceLEGD58eFSpUiXz6NKlSyxbtiymTp2auu7+/fvH7NmzM4+vv/56LW8dAABsOEqqNleXAwClSbmS7gCs6K677oolS5ZE/fr1M9OSJInc3Ny4+eabi7WMefPmxSmnnBL9+vUr9FqjRo1S58vNzY3c3NzV7zQAAPwJlVRtri4HAEoTITqlypIlS+Lf//53DB48OPbZZ5+s17p37x4PPvhgbLHFFvHcc89lvTZu3Lis5+3atYuPP/44mjdvvs77DAAAf0ZqcwCA3wnRKVWeeeaZ+OWXX6JPnz5RrVq1rNcOPfTQuOuuu+Lhhx+O6667Li644ILo06dPTJgwIYYPHx4RETk5ORERccEFF8ROO+0Up59+epx44olRuXLl+Pjjj2PkyJHFvmIGAAA2ZmpzAIDfuSc6pcpdd90VnTt3LlSkR/xeqL/77rsxd+7cePTRR+Pxxx+PNm3axC233BL/+Mc/IiIyX/ls06ZN/Pe//43PPvssdt9999huu+3i4osvzvoaKgAAkE5tDgDwu5wkSZKS7gSsqSuuuCJuvfXWtf6DQ3PmzIlq1arFgNe+iLwq+Wt12cDG58LtapV0F4BSoKC+mD17dlStWrWkuwNr3bqozdXl8OeiLgZKi+LW5m7nwgZp6NCh0aFDh6hZs2aMGTMmrrnmmjj99NNLulsAALDRUZsDAH92QnQ2SJMnT47LL788fv7552jUqFGcc8450b9//5LuFgAAbHTU5gDAn50QnQ3SkCFDYsiQISXdDQAA2OipzQGAPzs/LAoAAAAAACmE6AAAAAAAkEKIDgAAAAAAKYToAAAAAACQQogOAAAAAAAphOgAAAAAAJBCiA4AAAAAACmE6AAAAAAAkEKIDgAAAAAAKYToAAAAAACQQogOAAAAAAAphOgAAAAAAJBCiA4AAAAAACmE6AAAAAAAkEKIDgAAAAAAKYToAAAAAACQQogOAAAAAAAphOgAAAAAAJBCiA4AAAAAACmE6AAAAAAAkEKIDgAAAAAAKYToAAAAAACQQogOAAAAAAAphOgAAAAAAJBCiA4AAAAAACmE6AAAAAAAkEKIDgAAAAAAKYToAAAAAACQolxJdwA2BGe3rRlVq1Yt6W4AAMBGTV0OAJQEV6IDAAAAAEAKIToAAAAAAKQQogMAAAAAQAohOgAAAAAApBCiAwAAAABACiE6AAAAAACkEKIDAAAAAEAKIToAAAAAAKQQogMAAAAAQAohOgAAAAAApBCiAwAAAABACiE6AAAAAACkEKIDAAAAAEAKIToAAAAAAKQQogMAAAAAQAohOgAAAAAApBCiAwAAAABACiE6AAAAAACkKFfSHYANwXUTZ0ZelUUl3Q34U7pwu1ol3QUAYAOhLmdjoUYGKF1ciQ4AAAAAACmE6AAAAAAAkEKIDgAAAAAAKYToAAAAAACQQogOAAAAAAAphOgAAAAAAJBCiA4AAAAAACmE6AAAAAAAkEKIDgAAAAAAKYToAAAAAACQQogOAAAAAAAphOgAAAAAAJBCiA4AAAAAACmE6AAAAAAAkEKIDgAAAAAAKYToAAAAAACQQogOAAAAAAAphOgAAAAAAJBCiA4AAAAAACmE6AAAAAAAkEKIDgAAAAAAKYToAAAAAACQQogOAAAAAAAphOgAAAAAAJBCiA4AAAAAACmE6AAAAAAAkEKIDgAAAAAAKYToAAAAAACQQogOAAAAAAAphOgAAAAAAJBCiA4AAAAAACmE6AAAAAAAkEKIzgZr2rRpkZOTExMmTIiIiNGjR0dOTk7MmjWrRPsFAAAbg5ycnHjyySdLuhsAAOucEJ21qlevXtG9e/cSWfcuu+wS06dPj2rVqpXI+gEA2Lj06tUrcnJyIicnJypUqBDNmzePSy+9NJYsWVLSXVsvpk+fHvvuu29JdwMAYJ0rV9IdgLWlQoUKUbdu3ZLuBgAAG5GuXbvGsGHDYuHChfHcc89F3759o3z58tG/f/+sdosWLYoKFSqUUC/XDbU3ALCxcCU668yee+4Z/fr1i/PPPz9q1KgRdevWjUsuuSTzepIkcckll0SjRo0iNzc36tevH/369cu8XtTXQ6tXrx7Dhw8vcn0r3s5l+PDhUb169XjxxRdjyy23jCpVqkTXrl1j+vTpa3lLAQDYWOXm5kbdunWjcePGceqpp0bnzp3j6aefznxD84orroj69evHFltsERERX3/9dfTo0SOqV68eNWrUiIMOOiimTZuWWd6SJUuiX79+Ub169ahZs2ZccMEF0bNnz6xve66qzo6IuO6666J169ZRuXLlaNiwYZx22mkxb968zOvFrZXvvvvu2HrrrSM3Nzfq1asXp59+eua1Fev1VW3b6NGjY4cddojKlStH9erVY9ddd40vv/zyj+98AID1RIjOOnXPPfdE5cqV4+23346rr746Lr300hg5cmRERDz22GMxZMiQuO2222Ly5Mnx5JNPRuvWrdfq+ufPnx/XXntt3HvvvfHaa6/FV199Feeee+5aXQcAABSoWLFiLFq0KCIiRo0aFZMmTYqRI0fGM888E4sXL44uXbpEfn5+vP766zFmzJhMeF0wz1VXXRX3339/DBs2LMaMGRNz5swp8r7jK6uzIyLKlCkTN954Y3z00Udxzz33xCuvvBLnn39+1jJWVSvfcsst0bdv3zj55JPjgw8+iKeffjqaN29e5HavatuWLFkS3bt3j44dO8b7778fY8eOjZNPPjlycnLWdJcDAKxzbufCOtWmTZsYMGBARES0aNEibr755hg1alTsvffe8dVXX0XdunWjc+fOUb58+WjUqFHssMMOa3X9ixcvjltvvTWaNWsWERGnn356XHrppantFy5cGAsXLsw8nzNnzlrtDwAAf05JksSoUaPixRdfjDPOOCN+/PHHqFy5ctx5552Z27jcd999sWzZsrjzzjsz4fGwYcOievXqMXr06Nhnn33ipptuiv79+8fBBx8cERE333xzPPfcc4XWt7I6OyLirLPOyrRt0qRJXH755fHXv/41hg4dmpm+qlr58ssvj3POOSfOPPPMzLQOHToUuf0jRoxY6bZtv/32MXv27OjWrVtmfVtuuWXq/lSXAwCliSvRWafatGmT9bxevXoxY8aMiIg4/PDD47fffovNN988TjrppHjiiSfW+o8wVapUKVOkr7j+ogwaNCiqVauWeTRs2HCt9gcAgD+XZ555JqpUqRJ5eXmx7777xhFHHJG5tUrr1q2z7oM+ceLE+PzzzyM/Pz+qVKkSVapUiRo1asSCBQtiypQpMXv27Pjhhx+yLiwpW7ZstG/fvtB6V1ZnR0S8/PLL0alTp2jQoEHk5+fHcccdFzNnzoz58+dn2qysVp4xY0Z899130alTp2Lth1VtW40aNaJXr17RpUuXOOCAA+KGG25Y6W0W1eUAQGkiRGedKl++fNbznJycWLZsWURENGzYMCZNmhRDhw6NihUrxmmnnRZ77LFHLF68ONM2SZKs+QteW5P1r7jM5fXv3z9mz56deXz99dertT4AADYue+21V0yYMCEmT54cv/32W+Y2KxGR+W+BefPmRfv27WPChAlZj88++yyOPvro1VrvyursadOmRbdu3aJNmzbx2GOPxf/+97/417/+FRGRuW1M2jIKauWKFSuuVn+Ks23Dhg2LsWPHxi677BIjRoyIli1bxltvvVXk8tTlAEBp4nYulKiKFSvGAQccEAcccED07ds3WrVqFR988EG0a9cuateunXV1yuTJk7OunFkXcnNzIzc3d52uAwCAP4/KlSun3id8Re3atYsRI0bEpptuGlWrVi2yTZ06dWLcuHGxxx57RETE0qVL47333ottt9222H363//+F8uWLYvBgwdHmTK/Xzf18MMPF3v+iIj8/Pxo0qRJjBo1Kvbaa69Vti/OtkVEbLfddrHddttF//79Y+edd44HHnggdtppp0Lt1OUAQGniSnRKzPDhw+Ouu+6KDz/8ML744ou47777omLFitG4ceOIiPjLX/4SN998c4wfPz7efffd+Otf/1roahkAANhQHHPMMVGrVq046KCD4vXXX4+pU6fG6NGjo1+/fvHNN99ERMQZZ5wRgwYNiqeeeiomTZoUZ555Zvzyyy+r9QOczZs3j8WLF8dNN90UX3zxRdx7771x6623rnZ/L7nkkhg8eHDceOONMXny5Hjvvffipptu+kPbNnXq1Ojfv3+MHTs2vvzyy3jppZdi8uTJK70vOgBAaSFEp8RUr1497rjjjth1112jTZs28fLLL8d//vOfqFmzZkREDB48OBo2bBi77757HH300XHuuedGpUqVSrjXAADwx1SqVClee+21aNSoURxyyCGx5ZZbRp8+fWLBggWZq7cvuOCCOOqoo+L444+PnXfeOapUqRJdunSJvLy8Yq+nbdu2cd1118VVV10V22yzTdx///0xaNCg1e5vz5494/rrr4+hQ4fG1ltvHd26dYvJkyf/oW2rVKlSfPrpp3HooYdGy5Yt4+STT46+ffvGKaecstr9AgBY33KSld0gGjZyc+bMiWrVqsWA176IvCr5Jd0d+FO6cLtaJd0FgPWqoL6YPXv2Sm97ARERy5Ytiy233DJ69OgRl112WUl3p8Soy9nYqJEB1o/i1ubuiQ4AAFBKFNzqpGPHjrFw4cK4+eabY+rUqav9w6MAAKw9bucCAABQSpQpUyaGDx8eHTp0iF133TU++OCDePnll907HACgBLkSHQAAoJRo2LBhjBkzpqS7AQDAclyJDgAAAAAAKYToAAAAAACQQogOAAAAAAAphOgAAAAAAJBCiA4AAAAAACmE6AAAAAAAkEKIDgAAAAAAKYToAAAAAACQQogOAAAAAAAphOgAAAAAAJBCiA4AAAAAACmE6AAAAAAAkEKIDgAAAAAAKYToAAAAAACQQogOAAAAAAAphOgAAAAAAJBCiA4AAAAAACmE6AAAAAAAkEKIDgAAAAAAKYToAAAAAACQQogOAAAAAAAphOgAAAAAAJBCiA4AAAAAACmE6AAAAAAAkEKIDgAAAAAAKYToAAAAAACQQogOAAAAAAAphOgAAAAAAJBCiA4AAAAAACnKlXQHYENwdtuaUbVq1ZLuBgAAbNTU5QBASXAlOgAAAAAApBCiAwAAAABACiE6AAAAAACkEKIDAAAAAEAKIToAAAAAAKQQogMAAAAAQAohOgAAAAAApBCiAwAAAABACiE6AAAAAACkEKIDAAAAAEAKIToAAAAAAKQQogMAAAAAQAohOgAAAAAApBCiAwAAAABACiE6AAAAAACkEKIDAAAAAEAKIToAAAAAAKQQogMAAAAAQAohOgAAAAAApBCiAwAAAABACiE6AAAAAACkKFfSHYDSLEmSiIiYM2dOCfcEAPizKKgrCuoMYNXU5QDAulDc2lyIDisxc+bMiIho2LBhCfcEAPizmTt3blSrVq2kuwEbBHU5ALAurao2F6LDStSoUSMiIr766isnuRugOXPmRMOGDePrr7+OqlWrlnR3WA3GbsNm/DZsxm/dS5Ik5s6dG/Xr1y/prsAGQ11euvnsKN2MT+lmfEovY1O6ra3xKW5tLkSHlShT5vefDahWrZp/MDdgVatWNX4bKGO3YTN+Gzbjt24JAWH1qMs3DD47SjfjU7oZn9LL2JRua2N8ilOb+2FRAAAAAABIIUQHAAAAAIAUQnRYidzc3BgwYEDk5uaWdFf4A4zfhsvYbdiM34bN+AGlkX+bSjfjU7oZn9LN+JRexqZ0W9/jk5MkSbJe1gQAAAAAABsYV6IDAAAAAEAKIToAAAAAAKQQogMAAAAAQAohOhudf/3rX9GkSZPIy8uLHXfcMd55552Vtn/kkUeiVatWkZeXF61bt47nnnsu6/UkSeLiiy+OevXqRcWKFaNz584xefLkdbkJG621PXa9evWKnJycrEfXrl3X5SZs1FZn/D766KM49NBDo0mTJpGTkxPXX3/9Gi+TNbO2x++SSy4pdPy1atVqHW7Bxm11xu+OO+6I3XffPTbZZJPYZJNNonPnzoXa++wD1gZ1eemm9i691NWlm7q5dFMXl25re3zW5mePEJ2NyogRI+Lss8+OAQMGxHvvvRdt27aNLl26xIwZM4ps/+abb8ZRRx0Vffr0ifHjx0f37t2je/fu8eGHH2baXH311XHjjTfGrbfeGm+//XZUrlw5unTpEgsWLFhfm7VRWBdjFxHRtWvXmD59eubx4IMPro/N2eis7vjNnz8/Nt9887jyyiujbt26a2WZ/HHrYvwiIrbeeuus4++NN95YV5uwUVvd8Rs9enQcddRR8eqrr8bYsWOjYcOGsc8++8S3336baeOzD1hT6vLSTe1deqmrSzd1c+mmLi7d1sX4RKzFz54ENiI77LBD0rdv38zzpUuXJvXr108GDRpUZPsePXok+++/f9a0HXfcMTnllFOSJEmSZcuWJXXr1k2uueaazOuzZs1KcnNzkwcffHAdbMHGa22PXZIkSc+ePZODDjponfSXbKs7fstr3LhxMmTIkLW6TFbPuhi/AQMGJG3btl2LvSTNmh4rS5YsSfLz85N77rknSRKffcDaoS4v3dTepZe6unRTN5du6uLSbW2PT5Ks3c8eV6Kz0Vi0aFH873//i86dO2emlSlTJjp37hxjx44tcp6xY8dmtY+I6NKlS6b91KlT4/vvv89qU61atdhxxx1Tl8nqWxdjV2D06NGx6aabxhZbbBGnnnpqzJw5c+1vwEbuj4xfSSyToq3LfT158uSoX79+bL755nHMMcfEV199tabdZQVrY/zmz58fixcvjho1akSEzz5gzanLSze1d+mlri7d1M2lm7q4dFsX41NgbX32CNHZaPz000+xdOnSqFOnTtb0OnXqxPfff1/kPN9///1K2xf8d3WWyepbF2MX8ftXev7973/HqFGj4qqrror//ve/se+++8bSpUvX/kZsxP7I+JXEMinautrXO+64YwwfPjxeeOGFuOWWW2Lq1Kmx++67x9y5c9e0yyxnbYzfBRdcEPXr188UtD77gDWlLi/d1N6ll7q6dFM3l27q4tJtXYxPxNr97Cm32nMA/EkceeSRmf9v3bp1tGnTJpo1axajR4+OTp06lWDP4M9v3333zfx/mzZtYscdd4zGjRvHww8/HH369CnBnrG8K6+8Mh566KEYPXp05OXllXR3ANiAqb3hj1E3lw7q4tItbXzW5mePK9HZaNSqVSvKli0bP/zwQ9b0H374IfUHPOrWrbvS9gX/XZ1lsvrWxdgVZfPNN49atWrF559/vuadJuOPjF9JLJOira99Xb169WjZsqXjby1bk/G79tpr48orr4yXXnop2rRpk5nusw9YU+ry0k3tXXqpq0s3dXPppi4u3dbF+BRlTT57hOhsNCpUqBDt27ePUaNGZaYtW7YsRo0aFTvvvHOR8+y8885Z7SMiRo4cmWnftGnTqFu3blabOXPmxNtvv526TFbfuhi7onzzzTcxc+bMqFev3trpOBHxx8avJJZJ0dbXvp43b15MmTLF8beW/dHxu/rqq+Oyyy6LF154Ibbffvus13z2AWtKXV66qb1LL3V16aZuLt3UxaXbuhifoqzRZ89a+XlS2EA89NBDSW5ubjJ8+PDk448/Tk4++eSkevXqyffff58kSZIcd9xxyYUXXphpP2bMmKRcuXLJtddem3zyySfJgAEDkvLlyycffPBBps2VV16ZVK9ePXnqqaeS999/PznooIOSpk2bJr/99tt6374/s7U9dnPnzk3OPffcZOzYscnUqVOTl19+OWnXrl3SokWLZMGCBSWyjX9mqzt+CxcuTMaPH5+MHz8+qVevXnLuuecm48ePTyZPnlzsZbL2rIvxO+ecc5LRo0cnU6dOTcaMGZN07tw5qVWrVjJjxoz1vn1/dqs7fldeeWVSoUKF5NFHH02mT5+eecydOzerjc8+YE2oy0s3tXfppa4u3dTNpZu6uHRb2+Oztj97hOhsdG666aakUaNGSYUKFZIddtgheeuttzKvdezYMenZs2dW+4cffjhp2bJlUqFChWTrrbdOnn322azXly1bllx00UVJnTp1ktzc3KRTp07JpEmT1sembHTW5tjNnz8/2WeffZLatWsn5cuXTxo3bpycdNJJCsV1aHXGb+rUqUlEFHp07Nix2Mtk7Vrb43fEEUck9erVSypUqJA0aNAgOeKII5LPP/98PW7RxmV1xq9x48ZFjt+AAQMybXz2AWuDurx0U3uXXurq0k3dXLqpi0u3tTk+a/uzJydJkmT1r18HAAAAAIA/P/dEBwAAAACAFEJ0AAAAAABIIUQHAAAAAIAUQnQAAAAAAEghRAcAAAAAgBRCdAAAAAAASCFEBwAAAACAFEJ0AAAAAABIIUQHAAAAAIAUQnQA1kivXr0iJyen0OPzzz9fK8sfPnx4VK9efa0s64/q1atXdO/evUT7sDLTpk2LnJycmDBhQkl3BQCAEqIuL3nqcvjzKlfSHQBgw9e1a9cYNmxY1rTatWuXUG/SLV68OMqXL1/S3VirFi1aVNJdAACglFCXlxx1Ofy5uRIdgDWWm5sbdevWzXqULVs2IiKeeuqpaNeuXeTl5cXmm28eAwcOjCVLlmTmve6666J169ZRuXLlaNiwYZx22mkxb968iIgYPXp0nHDCCTF79uzMlTSXXHJJRETk5OTEk08+mdWP6tWrx/DhwyPi/18FMmLEiOjYsWPk5eXF/fffHxERd955Z2y55ZaRl5cXrVq1iqFDh67W9u65555xxhlnxFlnnRWbbLJJ1KlTJ+6444749ddf44QTToj8/Pxo3rx5PP/885l5Ro8eHTk5OfHss89GmzZtIi8vL3baaaf48MMPs5b92GOPxdZbbx25ubnRpEmTGDx4cNbrTZo0icsuuyyOP/74qFq1apx88snRtGnTiIjYbrvtIicnJ/bcc8+IiBg3blzsvffeUatWrahWrVp07Ngx3nvvvazl5eTkxJ133hkHH3xwVKpUKVq0aBFPP/10VpuPPvoounXrFlWrVo38/PzYfffdY8qUKZnX13R/AgCwdqjL1eXqclhHEgBYAz179kwOOuigIl977bXXkqpVqybDhw9PpkyZkrz00ktJkyZNkksuuSTTZsiQIckrr7ySTJ06NRk1alSyxRZbJKeeemqSJEmycOHC5Prrr0+qVq2aTJ8+PZk+fXoyd+7/a+/uQ2v++ziOv7ZpbMcc7WBN2OSuyabmdlgLyU2TJUUkahSlKTdJpGxmyD25ZzQ0k/wxRXL3hxMxMcluMEKGrNV+s7Ad7+uPK9/LsZ0xtlxd1/NRp77fz/dzPufz+fz1Ou++53v+MTMzSXb+/Hm/z3O73Zabm2tmZs+fPzdJFhsba+fOnbOKigp78+aNnTx50qKjo522c+fOWWRkpB0/fvyX15iSkmIRERGWlZVl5eXllpWVZSEhITZ58mQ7dOiQlZeX2+LFi83j8djHjx/NzOz69esmyeLi4uzy5cv28OFDS01NtdjYWPvy5YuZmRUVFVlwcLBlZmZaWVmZ5ebmWlhYmLMmM7OYmBjr1KmTbd261Z4+fWpPnz61O3fumCS7cuWKVVZWWlVVlZmZXb161fLy8qykpMQeP35s6enpFhUVZTU1Nc54kqxHjx52+vRpe/LkiWVkZFjHjh2dMV6/fm2RkZE2ffp0u3v3rpWVldmxY8estLTUzOy39hMAAACtj1xOLieXA22HIjoA4I/MmzfPQkJCzOVyOa8ZM2aYmdn48eNt48aNfv3z8vIsOjo64Hhnz541j8fjnOfm5prb7W7U71fD+s6dO/369OnTx06fPu3XlpWVZUlJSc2u8cewPmbMGOe8oaHBXC6XzZ0712mrrKw0SXbr1i0z+09Yz8/Pd/pUVVVZWFiYnTlzxszMZs+ebRMmTPD77JUrV9rAgQOd85iYGEtLS/Pr822t9+/fD7gGMzOfz2cRERFWWFjotEmytWvXOue1tbUmyS5evGhmZqtXr7bevXs7Xyh+9Dv7CQAAgNZHLieXk8uBtsMz0QEAf2zs2LHav3+/c+5yuSRJxcXF8nq9ys7Odq75fD59+vRJdXV1Cg8P15UrV5STk6PS0lLV1NSooaHB7/qfGjp0qHP88eNHPXv2TOnp6Vq4cKHT3tDQILfb3aJxExISnOOQkBB5PB7Fx8c7bVFRUZKk9+/f+70vKSnJOY6MjNSAAQNUUlIiSSopKdG0adP8+o8ePVo7d+6Uz+dzfor7/Zqa8+7dO61du1Y3btzQ+/fv5fP5VFdXp5cvXwZci8vlUqdOnZx5P3jwQMnJyU0+s7I19xMAAAB/jlxOLieXA22DIjoA4I+5XC717du3UXttba3Wr1+v6dOnN7rWoUMHvXjxQqmpqVq8eLGys7MVGRmpmzdvKj09XV++fGk2rAcFBcnM/Nrq6+ubnNv385Gkw4cPa8SIEX79vgXhX/VjeA0KCvJrCwoKkiR9/fq1ReP+iu/X1Jx58+apqqpKu3btUkxMjNq3b6+kpKRGf3rU1Fq+zTssLCzg+K25nwAAAPhz5HJyObkcaBsU0QEAbSYxMVFlZWVNBnlJunfvnr5+/apt27YpOPjf/3VdUFDg1yc0NFQ+n6/Re7t27arKykrn/MmTJ6qrq2t2PlFRUerevbsqKio0Z86cli6nVdy+fVu9evWSJFVXV6u8vFxxcXGSpLi4OHm9Xr/+Xq9X/fv3bzb8hoaGSlKjffJ6vdq3b5+mTJkiSXr16pU+fPjQovkmJCToxIkTqq+vbxTq/xv2EwAAAD9HLm+MXA6gJSiiAwDazLp165SamqpevXppxowZCg4OVnFxsR49eqQNGzaob9++qq+v1549ezR16lR5vV4dOHDAb4zY2FjV1tbq6tWrGjx4sMLDwxUeHq5x48Zp7969SkpKks/n06pVq5r8aeOP1q9fr4yMDLndbk2aNEmfP39WUVGRqqurtWzZsrbaCkdmZqY8Ho+ioqK0Zs0adenSRWlpaZKk5cuXa9iwYcrKytLMmTN169Yt7d27V/v27Wt2zG7duiksLEyXLl1Sjx491KFDB7ndbvXr1095eXkaOnSoampqtHLlymbvYGnKkiVLtGfPHs2aNUurV6+W2+3W7du3NXz4cA0YMOCv7ycAAAB+jlzeGLkcQEsE/+0JAAD+d02cOFEXLlzQ5cuXNWzYMI0cOVI7duxQTEyMJGnw4MHavn27Nm/erEGDBunUqVPKycnxG2PUqFFatGiRZs6cqa5du2rLli2SpG3btqlnz55KTk7W7NmztWLFil96VuOCBQt05MgR5ebmKj4+XikpKTp+/Lh69+7d+hvQhE2bNmnp0qUaMmSI3r59q8LCQueOlcTERBUUFCg/P1+DBg3SunXrlJmZqfnz5zc7Zrt27bR7924dPHhQ3bt3d57fePToUVVXVysxMVFz585VRkaGunXr1qL5ejweXbt2TbW1tUpJSdGQIUN0+PBh54vR395PAAAA/By5vDFyOYCWCLIfH1wFAABa3Y0bNzR27FhVV1erc+fOf3s6AAAAwP8lcjmA38Gd6AAAAAAAAAAABEARHQAAAAAAAACAAHicCwAAAAAAAAAAAXAnOgAAAAAAAAAAAVBEBwAAAAAAAAAgAIroAAAAAAAAAAAEQBEdAAAAAAAAAIAAKKIDAAAAAAAAABAARXQAAAAAAAAAAAKgiA4AAAAAAAAAQAAU0QEAAAAAAAAACIAiOgAAAAAAAAAAAfwLsI5voPYLRS8AAAAASUVORK5CYII=\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Sequential Backward Feature Selection\n",
        "classifiers = [RandomForestClassifier(), DecisionTreeClassifier()]\n",
        "selected_features = SequentialFeatureSelectionWarpper(classifiers, 5, x_train, y_train, forward=False)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 575
        },
        "id": "gvG_D6V1ldPd",
        "outputId": "af08e388-4e00-4556-d4a1-16877cb2d03a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1500x600 with 2 Axes>"
            ],
            "image/png": 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          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Sequential Floating Feature Selection\n",
        "\n",
        "Sequential floating feature selection (SFFS) is a feature selection technique that iteratively builds feature subsets by adding or removing features based on their impact on model performance. Unlike sequential forward or backward selection, SFFS allows for both addition and removal of features during each iteration, aiming to find the most relevant subset of features for the model. This method iteratively evaluates different combinations of features to determine the optimal subset that maximizes model performance."
      ],
      "metadata": {
        "id": "1ACidJKhxm2w"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def SequentialFloatingFeatureSelectionWarpper(classifiers, k_features, x_train, y_train, scoring=\"roc_auc\", forward=True, floating=True):\n",
        "    selected_features_all = []\n",
        "\n",
        "    fig, axs = plt.subplots(1, len(classifiers), figsize=(15, 6))\n",
        "\n",
        "    for i, classifier in enumerate(classifiers):\n",
        "        # Feature selection\n",
        "        model_wrapper = SequentialFeatureSelector(classifier, k_features, scoring=scoring, forward=forward, floating=floating)\n",
        "        mymodel = model_wrapper.fit(x_train, y_train)\n",
        "        selected_features_idx = list(mymodel.k_feature_idx_)\n",
        "        selected_features_all.append(selected_features_idx)\n",
        "\n",
        "        # Get feature names\n",
        "        feature_names = x_train.columns\n",
        "\n",
        "        # Visualizing feature importance\n",
        "        feature_importance = classifier.fit(x_train, y_train).feature_importances_\n",
        "        sorted_idx = feature_importance.argsort()[::-1]\n",
        "        top_k_idx = sorted_idx[:k_features]\n",
        "        top_k_features = [feature_names[idx] for idx in top_k_idx]\n",
        "        top_k_importance = feature_importance[top_k_idx]\n",
        "\n",
        "        axs[i].barh(top_k_features, top_k_importance, color='skyblue')\n",
        "        axs[i].set_xlabel('Feature Importance')\n",
        "        axs[i].set_title(f'{type(classifier).__name__} - Top Features Selected')\n",
        "        axs[i].invert_yaxis()\n",
        "\n",
        "    plt.tight_layout()\n",
        "    plt.show()\n",
        "\n",
        "    return selected_features_all"
      ],
      "metadata": {
        "id": "tSjnNnk-xtYv"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Sequential Floating Forward Feature Selection\n",
        "classifiers = [RandomForestClassifier(), DecisionTreeClassifier()]\n",
        "selected_features = SequentialFloatingFeatureSelectionWarpper(classifiers, 5, x_train, y_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 575
        },
        "id": "rgb5sDXLxtJz",
        "outputId": "8e252f43-d6ae-4586-874a-2c4b908d7b13"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1500x600 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Sequential Floating Backward Feature Selection\n",
        "classifiers = [RandomForestClassifier(), DecisionTreeClassifier()]\n",
        "selected_features = SequentialFloatingFeatureSelectionWarpper(classifiers, 5, x_train, y_train, forward=False)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 575
        },
        "id": "d7Ze2PL1zI9H",
        "outputId": "73258204-08a2-4b78-dd38-10b7c062403d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1500x600 with 2 Axes>"
            ],
            "image/png": 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          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Exhaustive Feature Selction\n",
        "\n",
        "Exhaustive feature selection is a feature selection technique that evaluates all possible feature combinations directly to determine the best feature subset. This method generates subsets of all features in the dataset and evaluates the performance of each subset, thereby selecting the most suitable feature set."
      ],
      "metadata": {
        "id": "7pk4A4T0lHAz"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "modelWrapperExhaustive = ExhaustiveFeatureSelector(RandomForestClassifier(), min_features =1, max_features=4)\n",
        "model = modelWrapperExhaustive.fit(x_train, y_train)\n",
        "list(model.best_idx_)"
      ],
      "metadata": {
        "id": "pBzR3CcwhzCI",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "e2cd71b9-be6b-46d9-b560-d40460e9d405"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Features: 162/162"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[0, 1, 3, 5]"
            ]
          },
          "metadata": {},
          "execution_count": 66
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "list(model.best_idx_)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "UugO09zxmx3S",
        "outputId": "ecceb793-c2e7-49b7-cd4c-759976f7d25c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[0, 1, 3, 5]"
            ]
          },
          "metadata": {},
          "execution_count": 67
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def ExhaustiveFeatureSelectionWrapper(classifiers, x_train, y_train, scoring=\"roc_auc\", min_features=1, max_features=4):\n",
        "    selected_features_all = []\n",
        "\n",
        "    fig, axs = plt.subplots(1, len(classifiers), figsize=(15, 6))\n",
        "\n",
        "    for i, classifier in enumerate(classifiers):\n",
        "        # Feature selection\n",
        "        model_wrapper = ExhaustiveFeatureSelector(classifier, scoring=scoring, min_features=min_features, max_features=max_features)\n",
        "        mymodel = model_wrapper.fit(x_train, y_train)\n",
        "        selected_features_idx = list(mymodel.best_idx_)\n",
        "        selected_features_all.append(selected_features_idx)\n",
        "\n",
        "        # Get feature names\n",
        "        feature_names = x_train.columns\n",
        "\n",
        "        # Visualizing feature importance\n",
        "        feature_importance = classifier.fit(x_train, y_train).feature_importances_\n",
        "        sorted_idx = feature_importance.argsort()[::-1]\n",
        "        top_k_idx = sorted_idx[:max_features]\n",
        "        top_k_features = [feature_names[idx] for idx in top_k_idx]\n",
        "        top_k_importance = feature_importance[top_k_idx]\n",
        "\n",
        "        axs[i].barh(top_k_features, top_k_importance, color='skyblue')\n",
        "        axs[i].set_xlabel('Feature Importance')\n",
        "        axs[i].set_title(f'{type(classifier).__name__} - Top Features Selected')\n",
        "        axs[i].invert_yaxis()\n",
        "\n",
        "    plt.tight_layout()\n",
        "    plt.show()\n",
        "\n",
        "    return selected_features_all\n"
      ],
      "metadata": {
        "id": "ZsUgWDUGobnr"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Exhaustive Feature Selction\n",
        "classifiers = [RandomForestClassifier(), DecisionTreeClassifier()]\n",
        "ExhaustiveFeatureSelectionWrapper(classifiers, x_train, y_train, min_features=1, max_features=5)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 610
        },
        "id": "VuQ4iQqco6PU",
        "outputId": "0690b153-b787-4f3f-fab6-37fd7545ebae"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Features: 218/218"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1500x600 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[[0, 1, 5, 6, 7], [1, 2, 4, 5, 7]]"
            ]
          },
          "metadata": {},
          "execution_count": 69
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "hQoyn0wcpI0d"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}