Feature_selection_load_wine.ipynb

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        "<a href=\"https://colab.research.google.com/github/OLSecret/Wine/blob/main/Feature_selection_load_wine.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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      "metadata": {
        "id": "jOhz2WHy7ZKd"
      },
      "outputs": [],
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "from sklearn.datasets import load_wine\n",
        "\n",
        "%matplotlib inline"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.rcParams[\"figure.figsize\"] = (10, 8)"
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      "execution_count": null,
      "outputs": []
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    {
      "cell_type": "code",
      "source": [
        "import pprint\n",
        "\n",
        "wine_data = load_wine()\n",
        "pprint.pprint(wine_data)\n",
        "\n",
        "print(len(wine_data.feature_names))\n",
        "print(wine_data.data.shape)"
      ],
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        {
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          "name": "stdout",
          "text": [
            "{'DESCR': '.. _wine_dataset:\\n'\n",
            "          '\\n'\n",
            "          'Wine recognition dataset\\n'\n",
            "          '------------------------\\n'\n",
            "          '\\n'\n",
            "          '**Data Set Characteristics:**\\n'\n",
            "          '\\n'\n",
            "          '    :Number of Instances: 178\\n'\n",
            "          '    :Number of Attributes: 13 numeric, predictive attributes and '\n",
            "          'the class\\n'\n",
            "          '    :Attribute Information:\\n'\n",
            "          ' \\t\\t- Alcohol\\n'\n",
            "          ' \\t\\t- Malic acid\\n'\n",
            "          ' \\t\\t- Ash\\n'\n",
            "          '\\t\\t- Alcalinity of ash  \\n'\n",
            "          ' \\t\\t- Magnesium\\n'\n",
            "          '\\t\\t- Total phenols\\n'\n",
            "          ' \\t\\t- Flavanoids\\n'\n",
            "          ' \\t\\t- Nonflavanoid phenols\\n'\n",
            "          ' \\t\\t- Proanthocyanins\\n'\n",
            "          '\\t\\t- Color intensity\\n'\n",
            "          ' \\t\\t- Hue\\n'\n",
            "          ' \\t\\t- OD280/OD315 of diluted wines\\n'\n",
            "          ' \\t\\t- Proline\\n'\n",
            "          '\\n'\n",
            "          '    - class:\\n'\n",
            "          '            - class_0\\n'\n",
            "          '            - class_1\\n'\n",
            "          '            - class_2\\n'\n",
            "          '\\t\\t\\n'\n",
            "          '    :Summary Statistics:\\n'\n",
            "          '    \\n'\n",
            "          '    ============================= ==== ===== ======= =====\\n'\n",
            "          '                                   Min   Max   Mean     SD\\n'\n",
            "          '    ============================= ==== ===== ======= =====\\n'\n",
            "          '    Alcohol:                      11.0  14.8    13.0   0.8\\n'\n",
            "          '    Malic Acid:                   0.74  5.80    2.34  1.12\\n'\n",
            "          '    Ash:                          1.36  3.23    2.36  0.27\\n'\n",
            "          '    Alcalinity of Ash:            10.6  30.0    19.5   3.3\\n'\n",
            "          '    Magnesium:                    70.0 162.0    99.7  14.3\\n'\n",
            "          '    Total Phenols:                0.98  3.88    2.29  0.63\\n'\n",
            "          '    Flavanoids:                   0.34  5.08    2.03  1.00\\n'\n",
            "          '    Nonflavanoid Phenols:         0.13  0.66    0.36  0.12\\n'\n",
            "          '    Proanthocyanins:              0.41  3.58    1.59  0.57\\n'\n",
            "          '    Colour Intensity:              1.3  13.0     5.1   2.3\\n'\n",
            "          '    Hue:                          0.48  1.71    0.96  0.23\\n'\n",
            "          '    OD280/OD315 of diluted wines: 1.27  4.00    2.61  0.71\\n'\n",
            "          '    Proline:                       278  1680     746   315\\n'\n",
            "          '    ============================= ==== ===== ======= =====\\n'\n",
            "          '\\n'\n",
            "          '    :Missing Attribute Values: None\\n'\n",
            "          '    :Class Distribution: class_0 (59), class_1 (71), class_2 (48)\\n'\n",
            "          '    :Creator: R.A. Fisher\\n'\n",
            "          '    :Donor: Michael Marshall (MARSHALL%PLU@io.arc.nasa.gov)\\n'\n",
            "          '    :Date: July, 1988\\n'\n",
            "          '\\n'\n",
            "          'This is a copy of UCI ML Wine recognition datasets.\\n'\n",
            "          'https://archive.ics.uci.edu/ml/machine-learning-databases/wine/wine.data\\n'\n",
            "          '\\n'\n",
            "          'The data is the results of a chemical analysis of wines grown in '\n",
            "          'the same\\n'\n",
            "          'region in Italy by three different cultivators. There are thirteen '\n",
            "          'different\\n'\n",
            "          'measurements taken for different constituents found in the three '\n",
            "          'types of\\n'\n",
            "          'wine.\\n'\n",
            "          '\\n'\n",
            "          'Original Owners: \\n'\n",
            "          '\\n'\n",
            "          'Forina, M. et al, PARVUS - \\n'\n",
            "          'An Extendible Package for Data Exploration, Classification and '\n",
            "          'Correlation. \\n'\n",
            "          'Institute of Pharmaceutical and Food Analysis and Technologies,\\n'\n",
            "          'Via Brigata Salerno, 16147 Genoa, Italy.\\n'\n",
            "          '\\n'\n",
            "          'Citation:\\n'\n",
            "          '\\n'\n",
            "          'Lichman, M. (2013). UCI Machine Learning Repository\\n'\n",
            "          '[https://archive.ics.uci.edu/ml]. Irvine, CA: University of '\n",
            "          'California,\\n'\n",
            "          'School of Information and Computer Science. \\n'\n",
            "          '\\n'\n",
            "          '.. topic:: References\\n'\n",
            "          '\\n'\n",
            "          '  (1) S. Aeberhard, D. Coomans and O. de Vel, \\n'\n",
            "          '  Comparison of Classifiers in High Dimensional Settings, \\n'\n",
            "          '  Tech. Rep. no. 92-02, (1992), Dept. of Computer Science and Dept. '\n",
            "          'of  \\n'\n",
            "          '  Mathematics and Statistics, James Cook University of North '\n",
            "          'Queensland. \\n'\n",
            "          '  (Also submitted to Technometrics). \\n'\n",
            "          '\\n'\n",
            "          '  The data was used with many others for comparing various \\n'\n",
            "          '  classifiers. The classes are separable, though only RDA \\n'\n",
            "          '  has achieved 100% correct classification. \\n'\n",
            "          '  (RDA : 100%, QDA 99.4%, LDA 98.9%, 1NN 96.1% (z-transformed '\n",
            "          'data)) \\n'\n",
            "          '  (All results using the leave-one-out technique) \\n'\n",
            "          '\\n'\n",
            "          '  (2) S. Aeberhard, D. Coomans and O. de Vel, \\n'\n",
            "          '  \"THE CLASSIFICATION PERFORMANCE OF RDA\" \\n'\n",
            "          '  Tech. Rep. no. 92-01, (1992), Dept. of Computer Science and Dept. '\n",
            "          'of \\n'\n",
            "          '  Mathematics and Statistics, James Cook University of North '\n",
            "          'Queensland. \\n'\n",
            "          '  (Also submitted to Journal of Chemometrics).\\n',\n",
            " 'data': array([[1.423e+01, 1.710e+00, 2.430e+00, ..., 1.040e+00, 3.920e+00,\n",
            "        1.065e+03],\n",
            "       [1.320e+01, 1.780e+00, 2.140e+00, ..., 1.050e+00, 3.400e+00,\n",
            "        1.050e+03],\n",
            "       [1.316e+01, 2.360e+00, 2.670e+00, ..., 1.030e+00, 3.170e+00,\n",
            "        1.185e+03],\n",
            "       ...,\n",
            "       [1.327e+01, 4.280e+00, 2.260e+00, ..., 5.900e-01, 1.560e+00,\n",
            "        8.350e+02],\n",
            "       [1.317e+01, 2.590e+00, 2.370e+00, ..., 6.000e-01, 1.620e+00,\n",
            "        8.400e+02],\n",
            "       [1.413e+01, 4.100e+00, 2.740e+00, ..., 6.100e-01, 1.600e+00,\n",
            "        5.600e+02]]),\n",
            " 'feature_names': ['alcohol',\n",
            "                   'malic_acid',\n",
            "                   'ash',\n",
            "                   'alcalinity_of_ash',\n",
            "                   'magnesium',\n",
            "                   'total_phenols',\n",
            "                   'flavanoids',\n",
            "                   'nonflavanoid_phenols',\n",
            "                   'proanthocyanins',\n",
            "                   'color_intensity',\n",
            "                   'hue',\n",
            "                   'od280/od315_of_diluted_wines',\n",
            "                   'proline'],\n",
            " 'frame': None,\n",
            " 'target': array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
            "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
            "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1,\n",
            "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
            "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
            "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2,\n",
            "       2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n",
            "       2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n",
            "       2, 2]),\n",
            " 'target_names': array(['class_0', 'class_1', 'class_2'], dtype='<U7')}\n",
            "13\n",
            "(178, 13)\n"
          ]
        }
      ]
    },
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      "source": [
        "\n",
        "\n",
        "df = pd.DataFrame(\n",
        "    data = wine_data.data,\n",
        "    columns = wine_data.feature_names\n",
        ")\n",
        "\n",
        "df['target'] = wine_data.target\n",
        "\n",
        "df.head(7)"
      ],
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      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   alcohol  malic_acid   ash  alcalinity_of_ash  magnesium  total_phenols  \\\n",
              "0    14.23        1.71  2.43               15.6      127.0           2.80   \n",
              "1    13.20        1.78  2.14               11.2      100.0           2.65   \n",
              "2    13.16        2.36  2.67               18.6      101.0           2.80   \n",
              "3    14.37        1.95  2.50               16.8      113.0           3.85   \n",
              "4    13.24        2.59  2.87               21.0      118.0           2.80   \n",
              "5    14.20        1.76  2.45               15.2      112.0           3.27   \n",
              "6    14.39        1.87  2.45               14.6       96.0           2.50   \n",
              "\n",
              "   flavanoids  nonflavanoid_phenols  proanthocyanins  color_intensity   hue  \\\n",
              "0        3.06                  0.28             2.29             5.64  1.04   \n",
              "1        2.76                  0.26             1.28             4.38  1.05   \n",
              "2        3.24                  0.30             2.81             5.68  1.03   \n",
              "3        3.49                  0.24             2.18             7.80  0.86   \n",
              "4        2.69                  0.39             1.82             4.32  1.04   \n",
              "5        3.39                  0.34             1.97             6.75  1.05   \n",
              "6        2.52                  0.30             1.98             5.25  1.02   \n",
              "\n",
              "   od280/od315_of_diluted_wines  proline  target  \n",
              "0                          3.92   1065.0       0  \n",
              "1                          3.40   1050.0       0  \n",
              "2                          3.17   1185.0       0  \n",
              "3                          3.45   1480.0       0  \n",
              "4                          2.93    735.0       0  \n",
              "5                          2.85   1450.0       0  \n",
              "6                          3.58   1290.0       0  "
            ],
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>alcohol</th>\n",
              "      <th>malic_acid</th>\n",
              "      <th>ash</th>\n",
              "      <th>alcalinity_of_ash</th>\n",
              "      <th>magnesium</th>\n",
              "      <th>total_phenols</th>\n",
              "      <th>flavanoids</th>\n",
              "      <th>nonflavanoid_phenols</th>\n",
              "      <th>proanthocyanins</th>\n",
              "      <th>color_intensity</th>\n",
              "      <th>hue</th>\n",
              "      <th>od280/od315_of_diluted_wines</th>\n",
              "      <th>proline</th>\n",
              "      <th>target</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>14.23</td>\n",
              "      <td>1.71</td>\n",
              "      <td>2.43</td>\n",
              "      <td>15.6</td>\n",
              "      <td>127.0</td>\n",
              "      <td>2.80</td>\n",
              "      <td>3.06</td>\n",
              "      <td>0.28</td>\n",
              "      <td>2.29</td>\n",
              "      <td>5.64</td>\n",
              "      <td>1.04</td>\n",
              "      <td>3.92</td>\n",
              "      <td>1065.0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>13.20</td>\n",
              "      <td>1.78</td>\n",
              "      <td>2.14</td>\n",
              "      <td>11.2</td>\n",
              "      <td>100.0</td>\n",
              "      <td>2.65</td>\n",
              "      <td>2.76</td>\n",
              "      <td>0.26</td>\n",
              "      <td>1.28</td>\n",
              "      <td>4.38</td>\n",
              "      <td>1.05</td>\n",
              "      <td>3.40</td>\n",
              "      <td>1050.0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>13.16</td>\n",
              "      <td>2.36</td>\n",
              "      <td>2.67</td>\n",
              "      <td>18.6</td>\n",
              "      <td>101.0</td>\n",
              "      <td>2.80</td>\n",
              "      <td>3.24</td>\n",
              "      <td>0.30</td>\n",
              "      <td>2.81</td>\n",
              "      <td>5.68</td>\n",
              "      <td>1.03</td>\n",
              "      <td>3.17</td>\n",
              "      <td>1185.0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>14.37</td>\n",
              "      <td>1.95</td>\n",
              "      <td>2.50</td>\n",
              "      <td>16.8</td>\n",
              "      <td>113.0</td>\n",
              "      <td>3.85</td>\n",
              "      <td>3.49</td>\n",
              "      <td>0.24</td>\n",
              "      <td>2.18</td>\n",
              "      <td>7.80</td>\n",
              "      <td>0.86</td>\n",
              "      <td>3.45</td>\n",
              "      <td>1480.0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>13.24</td>\n",
              "      <td>2.59</td>\n",
              "      <td>2.87</td>\n",
              "      <td>21.0</td>\n",
              "      <td>118.0</td>\n",
              "      <td>2.80</td>\n",
              "      <td>2.69</td>\n",
              "      <td>0.39</td>\n",
              "      <td>1.82</td>\n",
              "      <td>4.32</td>\n",
              "      <td>1.04</td>\n",
              "      <td>2.93</td>\n",
              "      <td>735.0</td>\n",
              "      <td>0</td>\n",
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              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>14.20</td>\n",
              "      <td>1.76</td>\n",
              "      <td>2.45</td>\n",
              "      <td>15.2</td>\n",
              "      <td>112.0</td>\n",
              "      <td>3.27</td>\n",
              "      <td>3.39</td>\n",
              "      <td>0.34</td>\n",
              "      <td>1.97</td>\n",
              "      <td>6.75</td>\n",
              "      <td>1.05</td>\n",
              "      <td>2.85</td>\n",
              "      <td>1450.0</td>\n",
              "      <td>0</td>\n",
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              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>14.39</td>\n",
              "      <td>1.87</td>\n",
              "      <td>2.45</td>\n",
              "      <td>14.6</td>\n",
              "      <td>96.0</td>\n",
              "      <td>2.50</td>\n",
              "      <td>2.52</td>\n",
              "      <td>0.30</td>\n",
              "      <td>1.98</td>\n",
              "      <td>5.25</td>\n",
              "      <td>1.02</td>\n",
              "      <td>3.58</td>\n",
              "      <td>1290.0</td>\n",
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              "          + ' to learn more about interactive tables.';\n",
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              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
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              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
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              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
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              "        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",
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            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df",
              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 178,\n  \"fields\": [\n    {\n      \"column\": \"alcohol\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.8118265380058575,\n        \"min\": 11.03,\n        \"max\": 14.83,\n        \"num_unique_values\": 126,\n        \"samples\": [\n          11.62,\n          13.64,\n          13.69\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"malic_acid\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.1171460976144627,\n        \"min\": 0.74,\n        \"max\": 5.8,\n        \"num_unique_values\": 133,\n        \"samples\": [\n          1.21,\n          2.83,\n          1.8\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"ash\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.27434400906081485,\n        \"min\": 1.36,\n        \"max\": 3.23,\n        \"num_unique_values\": 79,\n        \"samples\": [\n          2.31,\n          2.43,\n          2.52\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"alcalinity_of_ash\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 3.339563767173505,\n        \"min\": 10.6,\n        \"max\": 30.0,\n        \"num_unique_values\": 63,\n        \"samples\": [\n          25.5,\n          28.5,\n          15.6\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"magnesium\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 14.282483515295665,\n        \"min\": 70.0,\n        \"max\": 162.0,\n        \"num_unique_values\": 53,\n        \"samples\": [\n          126.0,\n          85.0,\n          162.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"total_phenols\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.6258510488339893,\n        \"min\": 0.98,\n        \"max\": 3.88,\n        \"num_unique_values\": 97,\n        \"samples\": [\n          1.68,\n          2.11,\n          1.35\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"flavanoids\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.9988586850169467,\n        \"min\": 0.34,\n        \"max\": 5.08,\n        \"num_unique_values\": 132,\n        \"samples\": [\n          3.18,\n          2.5,\n          3.17\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"nonflavanoid_phenols\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.12445334029667937,\n        \"min\": 0.13,\n        \"max\": 0.66,\n        \"num_unique_values\": 39,\n        \"samples\": [\n          0.58,\n          0.41,\n          0.39\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"proanthocyanins\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.5723588626747613,\n        \"min\": 0.41,\n        \"max\": 3.58,\n        \"num_unique_values\": 101,\n        \"samples\": [\n          0.75,\n          1.77,\n          1.42\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"color_intensity\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2.318285871822413,\n        \"min\": 1.28,\n        \"max\": 13.0,\n        \"num_unique_values\": 132,\n        \"samples\": [\n          2.95,\n          3.3,\n          5.1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"hue\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.22857156582982338,\n        \"min\": 0.48,\n        \"max\": 1.71,\n        \"num_unique_values\": 78,\n        \"samples\": [\n          1.22,\n          1.04,\n          1.45\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"od280/od315_of_diluted_wines\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.7099904287650504,\n        \"min\": 1.27,\n        \"max\": 4.0,\n        \"num_unique_values\": 122,\n        \"samples\": [\n          4.0,\n          1.82,\n          1.59\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"proline\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 314.9074742768491,\n        \"min\": 278.0,\n        \"max\": 1680.0,\n        \"num_unique_values\": 121,\n        \"samples\": [\n          1375.0,\n          1270.0,\n          735.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"target\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 2,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          0,\n          1,\n          2\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from seaborn import swarmplot\n",
        "\n",
        "data_to_plot = pd.melt(df[['alcohol', 'malic_acid', 'alcalinity_of_ash', 'color_intensity', 'target']],\n",
        "                       id_vars = 'target',\n",
        "                       var_name = 'features',\n",
        "                       value_name = 'value'\n",
        "                       )\n",
        "\n",
        "swarmplot(data = data_to_plot, x = 'features', y = 'value', hue = 'target');"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 781
        },
        "id": "hB3W7OuY8Y6R",
        "outputId": "3bbe5f03-5da3-470e-e677-65ab080e8510"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/seaborn/categorical.py:3398: UserWarning: 15.7% of the points cannot be placed; you may want to decrease the size of the markers or use stripplot.\n",
            "  warnings.warn(msg, UserWarning)\n",
            "/usr/local/lib/python3.10/dist-packages/seaborn/categorical.py:3398: UserWarning: 13.5% of the points cannot be placed; you may want to decrease the size of the markers or use stripplot.\n",
            "  warnings.warn(msg, UserWarning)\n",
            "/usr/local/lib/python3.10/dist-packages/seaborn/categorical.py:3398: UserWarning: 21.3% of the points cannot be placed; you may want to decrease the size of the markers or use stripplot.\n",
            "  warnings.warn(msg, UserWarning)\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x800 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Note:\n",
        "each of theese not enough to distinguish perfectly between categories, since for certain values of alcohol\\malic_acid\\... there are all three types of tagret 0,1,2"
      ],
      "metadata": {
        "id": "23mORWVn-zPB"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df['target'].value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "SfNsZVVN8iKT",
        "outputId": "c2f9b6f4-08ad-4b88-947f-8899bfc6848e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "target\n",
              "1    71\n",
              "0    59\n",
              "2    48\n",
              "Name: count, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "fig, ax = plt.subplots()\n",
        "\n",
        "x = [0, 1, 2]\n",
        "y = [59, 71, 48]\n",
        "bar_labels = ['0', '1', '2']\n",
        "bar_colors = ['tab:red', 'tab:purple', 'tab:orange']\n",
        "ax.bar(x, y, width = 0.4, label=bar_labels, color=bar_colors)\n",
        "\n",
        "ax.set_xlabel('Category')\n",
        "ax.set_ylabel('Count')\n",
        "ax.set_xticks([0, 1, 2])\n",
        "ax.set_xticklabels([0, 1, 2], fontsize = 12)\n",
        "ax.legend(title='Wine target')\n",
        "\n",
        "for idx, value in enumerate(y):\n",
        "  plt.text(x = int(idx), y = value + 1, s = str(value), ha = 'center')\n",
        "\n",
        "plt.tight_layout()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 650
        },
        "id": "ilaX-cys_umU",
        "outputId": "b6533541-b8b9-46cb-e681-93d7df8730b5"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x800 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "X = df.drop(['target'], axis = 1) # everything exept target is a feature\n",
        "y = df['target']\n",
        "\n",
        "X_train, X_test, y_train, y_test = train_test_split(X,\n",
        "                                                    y,\n",
        "                                                    test_size = 0.3,\n",
        "                                                    shuffle = True,\n",
        "                                                    stratify = y,\n",
        "                                                    random_state = 42)"
      ],
      "metadata": {
        "id": "-AfAbFijDBTD"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [],
      "metadata": {
        "id": "VRTg5emW-xor"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "print(X_train.shape)\n",
        "print(X_test.shape)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "UzAdL7B1DpQl",
        "outputId": "681f079c-1e82-4b11-9eff-d68df6097156"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(124, 13)\n",
            "(54, 13)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Baseline model: Gradient Boosting Classifier with all features"
      ],
      "metadata": {
        "id": "V56Cw0hJEAys"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.ensemble import GradientBoostingClassifier\n",
        "from sklearn.metrics import f1_score\n",
        "\n",
        "# Initialize and train classifier\n",
        "gbc = GradientBoostingClassifier(max_depth = 5, random_state = 42)\n",
        "gbc.fit(X_train, y_train)\n",
        "# Make predictions\n",
        "predictions = gbc.predict(X_test)\n",
        "# Evaluate the model with F1-score\n",
        "f1_score_all = round(f1_score(y_test, predictions, average = 'weighted'), 3)\n",
        "\n",
        "print(f1_score_all)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "maN1KbSID_g-",
        "outputId": "01bf60e7-74f0-4891-ff35-a297ce29c26b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0.908\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Feature selection techniques\n",
        "1 Variance threshold"
      ],
      "metadata": {
        "id": "Bxerzam0FqtQ"
      }
    },
    {
      "cell_type": "markdown",
      "source": [],
      "metadata": {
        "id": "Es2X78YOFwAB"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "X_train_v1, X_test_v1, y_train_v1, y_test_v1 = X_train.copy(), X_test.copy(), y_train.copy(), y_test.copy()\n",
        "\n",
        "#Calculate the variance of each feature\n",
        "\n",
        "X_train_v1.var(axis = 0)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ukaOwNOJD3-H",
        "outputId": "962e59da-cf0f-4204-b04a-7116430859a2"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "alcohol                             0.658341\n",
              "malic_acid                          1.123507\n",
              "ash                                 0.072433\n",
              "alcalinity_of_ash                  11.471279\n",
              "magnesium                         232.071532\n",
              "total_phenols                       0.393226\n",
              "flavanoids                          0.912299\n",
              "nonflavanoid_phenols                0.013873\n",
              "proanthocyanins                     0.335108\n",
              "color_intensity                     5.669722\n",
              "hue                                 0.052891\n",
              "od280/od315_of_diluted_wines        0.470021\n",
              "proline                         94906.710923\n",
              "dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Variance close to zero are not really predictive\n",
        "# Although to compare variance we need to scale features!\n",
        "\n",
        "from sklearn.preprocessing import MinMaxScaler\n",
        "\n",
        "scaler = MinMaxScaler()\n",
        "\n",
        "scaled_X_train_v1 = scaler.fit_transform(X_train_v1)"
      ],
      "metadata": {
        "id": "iOTtn8TuGhH5"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "fig, ax = plt.subplots()\n",
        "\n",
        "x = X.columns\n",
        "y = scaled_X_train_v1.var(axis = 0)\n",
        "\n",
        "#bar_labels = ['0', '1', '2']\n",
        "#bar_colors = ['tab:red', 'tab:purple', 'tab:orange']\n",
        "ax.bar(x, y, width = 0.4, color='green') #, label=bar_labels, color=bar_colors)\n",
        "\n",
        "ax.set_xlabel('Features')\n",
        "ax.set_ylabel('Variance')\n",
        "ax.set_ylim(0, 0.1)\n",
        "\n",
        "for idx, value in enumerate(y):\n",
        "  plt.text(x = idx, y = value + 0.001, s = str(round(value, 3)), ha = 'center')\n",
        "fig.autofmt_xdate()\n",
        "plt.tight_layout()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 650
        },
        "id": "dw8Vg55yHemQ",
        "outputId": "bb13b1b7-9e80-4d14-aec1-afcd7ede384c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x800 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [],
      "metadata": {
        "id": "CidHEgrnHFGo"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# try to set threshod to 0.03 to drop ash and magnesium\n",
        "\n",
        "selected_X_train_v1 = X_train_v1.drop(['ash', 'magnesium'], axis = 1)\n",
        "selected_X_test_v1 = X_test_v1.drop(['ash', 'magnesium'], axis = 1)\n",
        "\n",
        "gbc.fit(selected_X_train_v1, y_train_v1)\n",
        "var_predictions = gbc.predict(selected_X_test_v1)\n",
        "# Evaluate the model with F1-score\n",
        "f1_score_var = round(f1_score(y_test_v1, var_predictions, average = 'weighted'), 3)\n",
        "\n",
        "print(f1_score_var)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "s3_SIaO1IrhU",
        "outputId": "c832cc5f-61c2-4ead-ebc1-b4601ada9520"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0.963\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "results = {'all features': f1_score_all, \"set variance threshold to 0.3\": f1_score_var}\n",
        "print(results)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0lyjVYV9JffP",
        "outputId": "f8b0a7d9-6c5f-4f21-c164-8eee728905e3"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "{'all features': 0.908, 'set variance threshold to 0.3': 0.963}\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# try to set threshod to 0.031 to drop columns with the variance lower than automatically\n",
        "\n",
        "# Calculate the variance of each feature in the scaled training data\n",
        "variances = scaled_X_train_v1.var(axis=0)\n",
        "\n",
        "# Set your variance threshold\n",
        "variance_threshold = 0.031\n",
        "\n",
        "# Create a mask for columns with variance greater than the threshold\n",
        "selected_features = variances > variance_threshold\n",
        "\n",
        "# Apply this mask to select columns from the original X_train_v1 and X_test_v1\n",
        "selected_X_train_v2 = X_train_v1.iloc[:, selected_features]\n",
        "selected_X_test_v2 = X_test_v1.iloc[:, selected_features]\n",
        "\n",
        "gbc.fit(selected_X_train_v2, y_train_v1)\n",
        "var_predictions = gbc.predict(selected_X_test_v2)\n",
        "# Evaluate the model with F1-score\n",
        "f1_score_var2 = round(f1_score(y_test_v1, var_predictions, average = 'weighted'), 3)\n",
        "\n",
        "print(f1_score_var2)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vbJR8uYLLqT2",
        "outputId": "5ddedae9-e664-435c-c8a7-13fecadc33a4"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0.963\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from pprint import pprint\n",
        "results[\"set variance threshold to 0.31\"] = f1_score_var2\n",
        "pprint(results)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "AIrJIYpdMems",
        "outputId": "9c956712-8a6e-4dc2-9ef3-8dcfca29f7c3"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "{'all features': 0.908,\n",
            " 'set variance threshold to 0.3': 0.963,\n",
            " 'set variance threshold to 0.31': 0.963}\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "fig, ax = plt.subplots()\n",
        "\n",
        "x = results.keys()\n",
        "y = results.values()\n",
        "\n",
        "ax.bar(x, y, width = 0.4, color='purple')\n",
        "\n",
        "#ax.set_xlabel('Features')\n",
        "ax.set_ylabel('F1-Score weighted')\n",
        "ax.set_ylim(0, 1.2)\n",
        "\n",
        "for idx, value in enumerate(y):\n",
        "  plt.text(x = idx, y = value + 0.01, s = str(round(value, 3)), ha = 'center')\n",
        "#fig.autofmt_xdate()\n",
        "plt.tight_layout()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 656
        },
        "id": "kbeSw_oaMrIL",
        "outputId": "b32df890-cb50-4146-b84e-51f3a7ba2774"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x800 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "2 Filter method\n",
        "K-best features"
      ],
      "metadata": {
        "id": "Pt15O8UrOY7S"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "X_train_v3, X_test_v3, y_train_v3, y_test_v3 = X_train.copy(), X_test.copy(), y_train.copy(), y_test.copy()\n"
      ],
      "metadata": {
        "id": "frkRPS5SNnla"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.feature_selection import SelectKBest\n",
        "from sklearn.feature_selection import mutual_info_classif\n",
        "\n",
        "f1_score_list = []\n",
        "\n",
        "for k in range(1, 14):\n",
        "  selector = SelectKBest(mutual_info_classif, k = k)\n",
        "  selector.fit(X_train_v3, y_train_v3)\n",
        "\n",
        "  selected_X_train_v3 = selector.transform(X_train_v3)\n",
        "  selected_X_test_v3 = selector.transform(X_test_v3)\n",
        "\n",
        "  gbc.fit(selected_X_train_v3, y_train_v3)\n",
        "  kbest_predictions = gbc.predict(selected_X_test_v3)\n",
        "\n",
        "  f1_score_kbest = round(f1_score(y_test_v3, kbest_predictions, average = 'weighted'), 3)\n",
        "\n",
        "  f1_score_list.append(f1_score_kbest)"
      ],
      "metadata": {
        "id": "WZwNSMbTPDrx"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [],
      "metadata": {
        "id": "WaCvULiGPR6O"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "fig, ax = plt.subplots()\n",
        "\n",
        "x = np.arange(1, 14)\n",
        "y = f1_score_list\n",
        "\n",
        "ax.bar(x, y, width = 0.4, color='green')\n",
        "\n",
        "ax.set_xlabel('Number of features selected using mutual info')\n",
        "ax.set_ylabel('F1-Score weighted')\n",
        "ax.set_ylim(0, 1.2)\n",
        "ax.set_xticks(np.arange(1, 14))\n",
        "ax.set_xticklabels(np.arange(1, 14), fontsize = 12)\n",
        "\n",
        "for idx, value in enumerate(y):\n",
        "  plt.text(x = idx + 1, y = value + 0.05, s = str(value), ha = 'center')\n",
        "fig.autofmt_xdate()\n",
        "plt.tight_layout()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 650
        },
        "id": "ciyiRKX1PEbT",
        "outputId": "bd84d072-0598-4263-801c-4044c37639bf"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x800 with 1 Axes>"
            ],
            "image/png": 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6/vvv1ahRowLd/7+iFwDgGtynGwAAAACAPOI+3QAAAMAVTJ48WaGhofL29lZERITWrVt32WUvXbqkkSNHqnbt2vL29lZYWJgSEhIuu/yYMWNks9n01FNPOY3feOONstlsTl/9+/fPr126avQCsA6hGwAAACXOnDlzFBsbq7i4OG3YsEFhYWGKioq67K3Mhg4dqnfffVdvvvmmtm3bpv79++uuu+7Sxo0bsy37448/6t1331WTJk1y3Fa/fv105MgRx9d//vOffN23vKIXgLVcGrpXrFihrl27KigoSDabTfPnz//HdZYtW6ZmzZrJy8tLderU0Ycffmh5nQAAAChexo8fr379+ik6OloNGzbUlClTVLp0aU2fPj3H5WfOnKkXX3xRXbp0Ua1atfT444+rS5cuGjdunNNyKSkp6tmzp6ZOnaoKFSrkuK3SpUsrMDDQ8eXqSx7pBWAtl4bu1NRUhYWFafLkyblafv/+/br99tt10003adOmTXrqqaf0yCOPaPHixRZXCgAAgOLi4sWLWr9+vSIjIx1jbm5uioyM1Jo1a3JcJy0tTd7e3k5jPj4+WrlypdPYgAEDdPvttztt++8+/vhjVapUSddff72GDBmic+fOXcPeXBt6AVjPpbOX33bbbbrttttyvfyUKVNUs2ZNx6doDRo00MqVK/XGG28oKirKqjIBAABQjBw/flwZGRmOmdszBQQEaMeOHTmuExUVpfHjx6tDhw6qXbu2EhMTNW/ePGVkZDiWmT17tjZs2KAff/zxsq/94IMPqkaNGgoKCtLmzZv1wgsvaOfOnZo3b17+7Fwe0QvAekXqlmFr1qzJ9klZVFRUtkkZAAAAgPw0ceJE9evXT/Xr15fNZlPt2rUVHR3tOAX70KFDGjRokJYsWZLtKPBfPfroo47/b9y4sapWraqbb75Ze/fuVe3atS3fj/xAL4C8KVITqSUlJeX4KdyZM2d0/vz5HNdJS0vTmTNnnL4AAABQclWqVEnu7u5KTk52Gk9OTlZgYGCO61SuXFnz589XamqqDhw4oB07dsjX11e1atWSJK1fv15Hjx5Vs2bN5OHhIQ8PDy1fvlyTJk2Sh4eH01Hgv4qIiJAk7dmzJx/3MPfoBWC9IhW6r0Z8fLzKly/v+AoJCXF1SQAAAHAhT09PNW/eXImJiY4xu92uxMREtW7d+orrent7Kzg4WOnp6Zo7d67uvPNOSdLNN9+sLVu2aNOmTY6vFi1aqGfPntq0aZPc3d1z3N6mTZskSVWrVs2fncsjegFYr0idXh4YGJjjp3DlypWTj49PjusMGTJEsbGxjsdnzpwheAMAAJRwsbGx6t27t1q0aKFWrVppwoQJSk1NVXR0tCSpV69eCg4OVnx8vCRp7dq1Onz4sMLDw3X48GENHz5cdrtdzz//vCSpbNmyuv76651eo0yZMvL393eM7927V7NmzVKXLl3k7++vzZs36+mnn1aHDh0ue0utgkAvAGsVqdDdunVrLVq0yGlsyZIlV/wUzsvLS15eXlaXBgAAgCKkR48eOnbsmIYNG6akpCSFh4crISHBcSnjwYMH5eaWdVLohQsXNHToUO3bt0++vr7q0qWLZs6cKT8/v1y/pqenp7799ltHqA0JCdE999yjoUOH5vfu5Qm9AKxlM8YYV714SkqK45qNpk2bavz48brppptUsWJFVa9eXUOGDNHhw4f10UcfSfrzlmHXX3+9BgwYoL59+2rp0qUaOHCgFi5cmOvZy8+cOaPy5cvr9OnT3AcQAAAAAHBVcpstXXpN908//aSmTZuqadOmkv48taVp06YaNmyYJOnIkSM6ePCgY/maNWtq4cKFWrJkicLCwjRu3Di9//773C6smJo8ebJCQ0Pl7e2tiIgIrVu37rLLXrp0SSNHjlTt2rXl7e2tsLAwJSQkOC0THx+vli1bqmzZsqpSpYq6deumnTt3ZtvWmjVr1KlTJ5UpU0blypVThw4dLjtRHwAAAABciUuPdLsCR7qLhjlz5qhXr16aMmWKIiIiNGHCBH322WfauXOnqlSpkm35F154Qf/97381depU1a9fX4sXL1ZsbKxWr17t+FCnc+fOuv/++9WyZUulp6frxRdf1C+//KJt27apTJkykv4M3J07d9aQIUPUtWtXeXh46Oeff9add97JZQoAAAAAHHKbLQndKJQiIiLUsmVLvfXWW5L+nEUzJCRETz75pAYPHpxt+aCgIL300ksaMGCAY+yee+6Rj4+P/vvf/+b4GseOHVOVKlW0fPlydejQQZJ0ww036JZbbtErr7xiwV4BAAAAKC6KxOnlQE4uXryo9evXKzIy0jHm5uamyMhIrVmzJsd10tLS5O3t7TTm4+OjlStXXvZ1Tp8+LUmqWLGiJOno0aNau3atqlSpojZt2iggIEAdO3a84jYAAAAA4EoI3Sh0jh8/royMDMeMmZkCAgKUlJSU4zpRUVEaP368du/eLbvdriVLlmjevHk6cuRIjsvb7XY99dRTatu2rePWFfv27ZMkDR8+XP369VNCQoKaNWumm2++Wbt3787HPQQAAABQUhC6USxMnDhRdevWVf369eXp6amYmBhFR0c73d7irwYMGKBffvlFs2fPdozZ7XZJ0mOPPabo6Gg1bdpUb7zxhurVq6fp06cXyH4AAAAAKF4I3Sh0KlWqJHd3dyUnJzuNJycnKzAwMMd1KleurPnz5ys1NVUHDhzQjh075Ovrq1q1amVbNiYmRl999ZW+++47VatWzTFetWpVSVLDhg2dlm/QoIHTLPoAAAAAkFseri4A+DtPT081b95ciYmJ6tatm6Q/j0InJiYqJibmiut6e3srODhYly5d0ty5c9W9e3fHc8YYPfnkk/riiy+0bNky1axZ02nd0NBQBQUFZbuN2K5du3Tbbbflz87lE9sIW4G9lokr3HMt0oss9MIZ/chCLwAAcB1CNwql2NhY9e7dWy1atFCrVq00YcIEpaamKjo6WpLUq1cvBQcHKz4+XpK0du1aHT58WOHh4Tp8+LCGDx8uu92u559/3rHNAQMGaNasWVqwYIHKli3ruD68fPny8vHxkc1m03PPPae4uDiFhYUpPDxcM2bM0I4dO/T5558XfBMAAAAAFHmEbhRKPXr00LFjxzRs2DAlJSUpPDxcCQkJjsnVDh486HS99oULFzR06FDt27dPvr6+6tKli2bOnCk/Pz/HMu+8844k6cYbb3R6rQ8++EB9+vSRJD311FO6cOGCnn76aZ08eVJhYWFasmSJateuben+AgAAACieuKYbhVZMTIwOHDigtLQ0rV27VhEREY7nli1bpg8//NDxuGPHjtq2bZsuXLig48eP66OPPlJQUJDT9owxOX5lBu5MgwcP1qFDh5SamqrVq1erXbt2Vu4mAAAACpHJkycrNDRU3t7eioiI0Lp16y677KVLlzRy5EjVrl1b3t7eCgsLU0JCQp63mZSUpIceekiBgYEqU6aMmjVrprlz5+b7vl0N+nHtCN0AAAAAIGnOnDmKjY1VXFycNmzYoLCwMEVFReno0aM5Lj906FC9++67evPNN7Vt2zb1799fd911lzZu3Jinbfbq1Us7d+7Ul19+qS1btujuu+9W9+7dnbbjCvQjf9iMMSVqxpMzZ86ofPnyOn36tMqVK+fqcoCrwqRIWehFFnrhjH5koRcAkDsRERFq2bKl3nrrLUl/TuYbEhKiJ598UoMHD862fFBQkF566SUNGDDAMXbPPffIx8dH//3vf3O9TV9fX73zzjt66KGHHNvx9/fX2LFj9cgjj1i2v/+EflxZbrMlR7oBAAAAlHgXL17U+vXrFRkZ6Rhzc3NTZGSk1qxZk+M6aWlp8vb2dhrz8fHRypUr87TNNm3aaM6cOTp58qTsdrtmz56tCxcuZJuLqCDRj/xD6AYAAABQ4h0/flwZGRmOiXszBQQEOO5683dRUVEaP368du/eLbvdriVLlmjevHk6cuRInrb56aef6tKlS/L395eXl5cee+wxffHFF6pTp04+72Xu0Y/8Q+gGAAAAgKswceJE1a1bV/Xr15enp6diYmIUHR3tdJed3Hj55Zd16tQpffvtt/rpp58UGxur7t27a8uWLRZVbg36kTNuGQYAAACgxKtUqZLc3d2VnJzsNJ6cnKzAwMAc16lcubLmz5+vCxcu6MSJEwoKCtLgwYNVq1atXG9z7969euutt/TLL7+oUaNGkqSwsDB9//33mjx5sqZMmZLfu5or9CP/cKQbAAAAQInn6emp5s2bKzEx0TFmt9uVmJio1q1bX3Fdb29vBQcHKz09XXPnztWdd96Z622eO3dOkrIdDXZ3d5fdbs+Xfbsa9CP/cKQbAACUKAU1m3tRmMmdme0BZ7Gxserdu7datGihVq1aacKECUpNTVV0dLSkP29lFRwcrPj4eEnS2rVrdfjwYYWHh+vw4cMaPny47Ha7nn/++Vxvs379+qpTp44ee+wxvf766/L399f8+fO1ZMkSffXVVwXfhL+gH/mD0A0AAAAAknr06KFjx45p2LBhSkpKUnh4uBISEhwTfx08eNDpCOyFCxc0dOhQ7du3T76+vurSpYtmzpwpPz+/XG+zVKlSWrRokQYPHqyuXbsqJSVFderU0YwZM9SlS5cC3f+/ox/5g/t0A0UQRyay0Iss9MIZ/chCL5xxpDsL7w0AuHrcpxsAAAAAABcjdAMAAAAAYBFCNwAAAAAAFiF0AwAAAABgEUI3AAAAAAAWIXQDAAAAAGARQjcAAAAAABbxcHUBAAAAgKtxz3Jn9APIPxzpBgAAAADAIoRuAAAAAAAsQugGAAAAAMAihG4AAAAAACxC6AYAAAAAwCKEbgAAAAAALELoBgAAAADAIoRuAAAAAAAsQugGAAAAAMAihG4AAAAAACzi4eoCgNywjbAV2GuZOFNgrwUAAACgeONINwAAAAAAFiF0AwAAAABgEUI3AAAAAAAWIXQDAAAAAGARQjcAAAAAABYhdAMAAAAAYBFCNwAAAAAAFiF0AwAAAABgEUI3AAAAAAAWIXQDAAAAAGARD1cXAAAAAACFlW2ErcBey8SZAnutq0Evrg5HugEAAAAAsAihGwAAAAAAixC6AQAAAACwCKEbAAAAAACLELoBAAAAALAIoRsAAAAAAIsQugEAAAAAsAihGwAAAAAAixC6AQAAAACwCKEbAAAAAACLELoBAAAAALAIoRsAAAAAAIsQugEAAAAAsAihGwAAAAAAixC6AQAAAACwCKEbAAAAAACLELoBAAAAALAIoRsAAAAAAIsQugEAAAAAsAihGwAAAAAAixC6AQAAAACwCKEbAAAAAACLELoBAAAAALAIoRsAAAAAAIsQugEAAAAAsAihGwAAAAAAixC6AQAAAACwCKEbAAAAAACLELoBAAAAALAIoRsAAAAAAIsQugEAAAAAsAihGwAAAAAAixC6AQAAAACwCKEbAAAAAACLELoBAAAAALAIoRsAAAAAAIsQugEAAAAAsAihGwAAAAAAixC6AQAAAACwCKEbAAAAAACLELoBAAAAALAIoRsAAAAAAIsQugEAAAAAsAihGwAAAAAAixC6AQAAAACwCKEbAAAAAACLELoBAAAAALAIoRsAAAAAAIsQuguZyZMnKzQ0VN7e3oqIiNC6desuu+yNN94om82W7ev22293LNOnT59sz3fu3NlpOxs2bNAtt9wiPz8/+fv769FHH1VKSopl+wgAAAAAJQWhuxCZM2eOYmNjFRcXpw0bNigsLExRUVE6evRojsvPmzdPR44ccXz98ssvcnd313333ee0XOfOnZ2W++STTxzP/f7774qMjFSdOnW0du1aJSQkaOvWrerTp4+VuwoAAAAAJYKHqwtAlvHjx6tfv36Kjo6WJE2ZMkULFy7U9OnTNXjw4GzLV6xY0enx7NmzVbp06Wyh28vLS4GBgTm+5ldffaVSpUpp8uTJcnNzc7xukyZNtGfPHtWpUyc/dg0AAAAASiSOdBcSFy9e1Pr16xUZGekYc3NzU2RkpNasWZOrbUybNk3333+/ypQp4zS+bNkyValSRfXq1dPjjz+uEydOOJ5LS0uTp6enI3BLko+PjyRp5cqV17JLAAAAAFDiEboLiePHjysjI0MBAQFO4wEBAUpKSvrH9detW6dffvlFjzzyiNN4586d9dFHHykxMVFjx47V8uXLddtttykjI0OS1KlTJyUlJem1117TxYsX9ccffziOqh85ciSf9g4AAAAASiZCdzExbdo0NW7cWK1atXIav//++3XHHXeocePG6tatm7766iv9+OOPWrZsmSSpUaNGmjFjhsaNG6fSpUsrMDBQNWvWVEBAgNPRbwAAAABA3pGqColKlSrJ3d1dycnJTuPJycmXvR47U2pqqmbPnq2HH374H1+nVq1aqlSpkvbs2eMYe/DBB5WUlKTDhw/rxIkTGj58uI4dO6ZatWpd3c4AAAAAACQRugsNT09PNW/eXImJiY4xu92uxMREtW7d+orrfvbZZ0pLS9O///3vf3yd3377TSdOnFDVqlWzPRcQECBfX1/NmTNH3t7euuWWW/K+IwAAAAAAB2YvL0RiY2PVu3dvtWjRQq1atdKECROUmprqmM28V69eCg4OVnx8vNN606ZNU7du3eTv7+80npKSohEjRuiee+5RYGCg9u7dq+eff1516tRRVFSUY7m33npLbdq0ka+vr5YsWaLnnntOY8aMkZ+fn+X7DAAAAADFGaG7EOnRo4eOHTumYcOGKSkpSeHh4UpISHBMrnbw4MFs11nv3LlTK1eu1DfffJNte+7u7tq8ebNmzJihU6dOKSgoSLfeeqteeeUVeXl5OZZbt26d4uLilJKSovr16+vdd9/VQw89ZO3OAgAAAEAJ4PLQPXnyZL322mtKSkpSWFiY3nzzzWyTgf3VhAkT9M477+jgwYOqVKmS7r33XsXHx8vb27sAq7ZOTEyMYmJicnwuc/Kzv6pXr56MMTku7+Pjo8WLF//ja3700Ud5qhEAAAAAkDsuvaZ7zpw5io2NVVxcnDZs2KCwsDBFRUXp6NGjOS4/a9YsDR48WHFxcdq+fbumTZumOXPm6MUXXyzgygEAAAAA+GcuDd3jx49Xv379FB0drYYNG2rKlCkqXbq0pk+fnuPyq1evVtu2bfXggw8qNDRUt956qx544AGtW7eugCsHAAAAAOCfuSx0X7x4UevXr1dkZGRWMW5uioyM1Jo1a3Jcp02bNlq/fr0jZO/bt0+LFi1Sly5dCqRmAAAAAADywmXXdB8/flwZGRmOScIyBQQEaMeOHTmu8+CDD+r48eNq166djDFKT09X//79r3h6eVpamtLS0hyPz5w5kz87AAAAAADAPyhS9+letmyZRo8erbffflsbNmzQvHnztHDhQr3yyiuXXSc+Pl7ly5d3fIWEhBRgxQAAAACAksxlR7orVaokd3d3JScnO40nJycrMDAwx3VefvllPfTQQ3rkkUckSY0bN1ZqaqoeffRRvfTSS9lupyVJQ4YMUWxsrOPxmTNnCN4AAAAAgALhsiPdnp6eat68uRITEx1jdrtdiYmJat26dY7rnDt3Lluwdnd3l6TL3jbLy8tL5cqVc/oCAAAAAKAguPQ+3bGxserdu7datGihVq1aacKECUpNTVV0dLQkqVevXgoODlZ8fLwkqWvXrho/fryaNm2qiIgI7dmzRy+//LK6du3qCN8AAAAAABQWLg3dPXr00LFjxzRs2DAlJSUpPDxcCQkJjsnVDh486HRke+jQobLZbBo6dKgOHz6sypUrq2vXrho1apSrdgEAAAAAgMtyaeiWpJiYGMXExOT43LJly5wee3h4KC4uTnFxcQVQGQAAAAAA16ZIzV4OAAAAAEBRQugGAAAAAMAihG4AAAAAACxC6AYAAAAAwCKEbgAAAAAALELoBgAAAADAIoRuAAAAAAAsQugGAAAAAMAihG4AAAAAACxC6AYAAAAAwCKEbgAAAAAALELoBgAAAADAIoRuAAAAAAAsQugGAAAAAMAihG4AAAAAACxC6AYAAAAAwCKEbgAAAAAALELoBgAAAADAIh6uLgCXZxthK7DXMnGmwF4LAAAAAEoKjnQDAAAAAGARQjcAAAAAABYhdAMAAAAAYBFCNwAAAAAAFiF0AwAAAABgEUI3AAAAAAAWIXQDAAAAAGARQjcAAAAAABYhdAMAAAAAYBFCNwAAAAAAFiF0AwAAAABgEUI3AAAAAAAWIXQDAAAAAGARQjcAAAAAABYhdAMAAAAAYBFCNwAAAAAAFiF0AwAAAABgEUI3AAAAAAAWIXQDAAAAAGARQjcAAAAAABYhdAMAAAAAYBFCNwAAAAAAFiF0AwAAAABgEUI3AAAAAAAWIXQDAAAAAGARQjcAAAAAABYhdAMAAAAAYBFCNwAAAAAAFiF0AwAAAABgEUI3AAAAAAAWIXQDAAAAAGARj9wsVKFCBdlstlxt8OTJk9dUEAAAAAAAxUWuQveECRMc/3/ixAm9+uqrioqKUuvWrSVJa9as0eLFi/Xyyy9bUiQAAAAAAEVRrkJ37969Hf9/zz33aOTIkYqJiXGMDRw4UG+99Za+/fZbPf300/lfJQAAAAAARVCer+levHixOnfunG28c+fO+vbbb/OlKAAAAAAAioM8h25/f38tWLAg2/iCBQvk7++fL0UBAAAAAFAc5Or08r8aMWKEHnnkES1btkwRERGSpLVr1yohIUFTp07N9wIBAAAAACiq8hy6+/TpowYNGmjSpEmaN2+eJKlBgwZauXKlI4QDAAAAAICrCN2SFBERoY8//ji/awEAAAAAoFjJ8zXdkrR3714NHTpUDz74oI4ePSpJ+vrrr7V169Z8LQ4AAAAAgKIsz6F7+fLlaty4sdauXau5c+cqJSVFkvTzzz8rLi4u3wsEAAAAAKCoynPoHjx4sF599VUtWbJEnp6ejvFOnTrphx9+yNfiAAAAAAAoyvIcurds2aK77ror23iVKlV0/PjxfCkKAAAAAIDiIM+h28/PT0eOHMk2vnHjRgUHB+dLUQAAAAAAFAd5Dt3333+/XnjhBSUlJclms8lut2vVqlV69tln1atXLytqBAAAAACgSMpz6B49erTq16+vkJAQpaSkqGHDhurQoYPatGmjoUOHWlEjAAAAAABFUp7v0+3p6ampU6dq2LBh2rJli1JSUtS0aVPVrVvXivoAAAAAACiy8nyke+TIkTp37pxCQkLUpUsXde/eXXXr1tX58+c1cuRIK2oEAAAAAKBIynPoHjFihOPe3H917tw5jRgxIl+KAgAAAACgOMhz6DbGyGazZRv/+eefVbFixXwpCgAAAACA4iDX13RXqFBBNptNNptN1113nVPwzsjIUEpKivr3729JkQAAAAAAFEW5Dt0TJkyQMUZ9+/bViBEjVL58ecdznp6eCg0NVevWrS0pEgAAAACAoijXobt3796SpJo1a6pNmzYqVaqUZUUBAAAAAFAc5PmWYR07dpTdbteuXbt09OhR2e12p+c7dOiQb8UBAAAAAFCU5Tl0//DDD3rwwQd14MABGWOcnrPZbMrIyMi34gAAAAAAKMryHLr79++vFi1aaOHChapatWqOM5kDAAAAAICrCN27d+/W559/rjp16lhRDwAAAAAAxUae79MdERGhPXv2WFELAAAAAADFSq6OdG/evNnx/08++aSeeeYZJSUlqXHjxtlmMW/SpEn+VggAAAAAQBGVq9AdHh4um83mNHFa3759Hf+f+RwTqQEAAAAAkCVXoXv//v1W1wEAAAAAQLGTq9Bdo0YNq+sAAAAAAKDYyfPs5V9++WWO4zabTd7e3qpTp45q1qx5zYUBAAAAAFDU5Tl0d+vWLdv13ZLzdd3t2rXT/PnzVaFChXwrFAAAAACAoibPtwxbsmSJWrZsqSVLluj06dM6ffq0lixZooiICH311VdasWKFTpw4oWeffdaKegEAAAAAKDLyfKR70KBBeu+999SmTRvH2M033yxvb289+uij2rp1qyZMmOA0uzkAAAAAACVRno907927V+XKlcs2Xq5cOe3bt0+SVLduXR0/fvzaqwMAAAAAoAjLc+hu3ry5nnvuOR07dswxduzYMT3//PNq2bKlJGn37t0KCQnJvyoBAAAAACiC8nx6+bRp03TnnXeqWrVqjmB96NAh1apVSwsWLJAkpaSkaOjQoflbKQAAAAAARUyeQ3e9evW0bds2ffPNN9q1a5dj7JZbbpGb258Hzrt165avRQIAAAAAUBTlOXRLkpubmzp37qzOnTvndz0AAAAAABQbuQrdkyZN0qOPPipvb29NmjTpissOHDgwXwoDAAAAAKCoy1XofuONN9SzZ095e3vrjTfeuOxyNpuN0A0AAAAAwP/LVejev39/jv8PAAAAAAAuL8+3DMt08eJF7dy5U+np6flZDwAAAAAAxUaeQ/e5c+f08MMPq3Tp0mrUqJEOHjwoSXryySc1ZsyYfC8QAAAAAICiKs+he8iQIfr555+1bNkyeXt7O8YjIyM1Z86cfC0OAAAAAICiLM+3DJs/f77mzJmjG264QTabzTHeqFEj7d27N1+LAwAAAACgKMvzke5jx46pSpUq2cZTU1OdQjgAAAAAACVdnkN3ixYttHDhQsfjzKD9/vvvq3Xr1vlXGQAAAAAARVyeTy8fPXq0brvtNm3btk3p6emaOHGitm3bptWrV2v58uVW1AgAAAAAQJGU5yPd7dq106ZNm5Senq7GjRvrm2++UZUqVbRmzRo1b97cihoBAAAAACiS8nykW5Jq166tqVOn5nctAAAAAAAUK3k+0t2rVy998MEH2rdvnxX1AAAAAABQbOQ5dHt6eio+Pl516tRRSEiI/v3vf+v999/X7t27ragPAAAAAIAiK8+h+/3339euXbt06NAh/ec//5Gvr6/GjRun+vXrq1q1albUCAAAAABAkZTn0J2pQoUK8vf3V4UKFeTn5ycPDw9Vrlw5P2sDAAAAAKBIy3PofvHFF9WmTRv5+/tr8ODBunDhggYPHqykpCRt3LjRihoBAAAAACiS8jx7+ZgxY1S5cmXFxcXp7rvv1nXXXWdFXQAAAAAAFHl5PtK9ceNGvfTSS1q3bp3atm2r4OBgPfjgg3rvvfe0a9euPBcwefJkhYaGytvbWxEREVq3bt0Vlz916pQGDBigqlWrysvLS9ddd50WLVqU59cFAAAAAMBqeT7SHRYWprCwMA0cOFCS9PPPP+uNN97QgAEDZLfblZGRkettzZkzR7GxsZoyZYoiIiI0YcIERUVFaefOnapSpUq25S9evKhbbrlFVapU0eeff67g4GAdOHBAfn5+ed0NAAAAAAAsl+fQbYzRxo0btWzZMi1btkwrV67UmTNn1KRJE3Xs2DFP2xo/frz69eun6OhoSdKUKVO0cOFCTZ8+XYMHD862/PTp03Xy5EmtXr1apUqVkiSFhobmdRcAAAAAACgQeQ7dFStWVEpKisLCwtSxY0f169dP7du3z/PR5osXL2r9+vUaMmSIY8zNzU2RkZFas2ZNjut8+eWXat26tQYMGKAFCxaocuXKevDBB/XCCy/I3d09x3XS0tKUlpbmeHzmzJk81QkAAAAAwNXKc+j+73//q/bt26tcuXLX9MLHjx9XRkaGAgICnMYDAgK0Y8eOHNfZt2+fli5dqp49e2rRokXas2ePnnjiCV26dElxcXE5rhMfH68RI0ZcU60AAAAAAFyNPE+kdvvtt19z4L5adrtdVapU0XvvvafmzZurR48eeumllzRlypTLrjNkyBCdPn3a8XXo0KECrBgAAAAAUJLl+Uh3fqlUqZLc3d2VnJzsNJ6cnKzAwMAc16latapKlSrldCp5gwYNlJSUpIsXL8rT0zPbOl5eXvLy8srf4gEAAAAAyIU8H+nOL56enmrevLkSExMdY3a7XYmJiWrdunWO67Rt21Z79uyR3W53jO3atUtVq1bNMXADAAAAAOBKLgvdkhQbG6upU6dqxowZ2r59ux5//HGlpqY6ZjPv1auX00Rrjz/+uE6ePKlBgwZp165dWrhwoUaPHq0BAwa4ahcAAAAAALgsl51eLkk9evTQsWPHNGzYMCUlJSk8PFwJCQmOydUOHjwoN7eszwVCQkK0ePFiPf3002rSpImCg4M1aNAgvfDCC67aBQAAAAAALuuqQvfMmTM1ZcoU7d+/X2vWrFGNGjU0YcIE1axZU3feeWeethUTE6OYmJgcn1u2bFm2sdatW+uHH364mrIBAAAAAChQeT69/J133lFsbKy6dOmiU6dOKSMjQ5Lk5+enCRMm5Hd9AAAAAAAUWXkO3W+++aamTp2ql156yWkW8RYtWmjLli35WhwAAAAAAEVZnkP3/v371bRp02zjXl5eSk1NzZeiAAAAAAAoDvIcumvWrKlNmzZlG09ISFCDBg3yoyYAAAAAAIqFPE+kFhsbqwEDBujChQsyxmjdunX65JNPFB8fr/fff9+KGgEAAAAAKJLyHLofeeQR+fj4aOjQoTp37pwefPBBBQUFaeLEibr//vutqBEAAAAAgCIpT6E7PT1ds2bNUlRUlHr27Klz584pJSVFVapUsao+AAAAAACKrDxd0+3h4aH+/fvrwoULkqTSpUsTuAEAAAAAuIw8T6TWqlUrbdy40YpaAAAAAAAoVvJ8TfcTTzyhZ555Rr/99puaN2+uMmXKOD3fpEmTfCsOAAAAAICiLM+hO3OytIEDBzrGbDabjDGy2WzKyMjIv+oAAAAAACjC8hy69+/fb0UdAAAAAAAUO3kO3TVq1LCiDgAAAAAAip08h25J2rt3ryZMmKDt27dLkho2bKhBgwapdu3a+VocAAAAAABFWZ5nL1+8eLEaNmyodevWqUmTJmrSpInWrl2rRo0aacmSJVbUCAAAAABAkZTnI92DBw/W008/rTFjxmQbf+GFF3TLLbfkW3EAAAAAABRleT7SvX37dj388MPZxvv27att27blS1EAAAAAABQHeQ7dlStX1qZNm7KNb9q0SVWqVMmPmgAAAAAAKBbyfHp5v3799Oijj2rfvn1q06aNJGnVqlUaO3asYmNj871AAAAAAACKqjyH7pdffllly5bVuHHjNGTIEElSUFCQhg8froEDB+Z7gQAAAAAAFFV5Dt02m01PP/20nn76aZ09e1aSVLZs2XwvDAAAAACAoi7PoXv//v1KT09X3bp1ncL27t27VapUKYWGhuZnfQAAAAAAFFl5nkitT58+Wr16dbbxtWvXqk+fPvlREwAAAAAAxUKeQ/fGjRvVtm3bbOM33HBDjrOaAwAAAABQUuU5dNtsNse13H91+vRpZWRk5EtRAAAAAAAUB3kO3R06dFB8fLxTwM7IyFB8fLzatWuXr8UBAAAAAFCU5XkitbFjx6pDhw6qV6+e2rdvL0n6/vvvdebMGS1dujTfCwQAAAAAoKjK85Huhg0bavPmzerevbuOHj2qs2fPqlevXtqxY4euv/56K2oEAAAAAKBIyvORbkkKCgrS6NGj87sWAAAAAACKlVwf6T5+/LgOHDjgNLZ161ZFR0ere/fumjVrVr4XBwAAAABAUZbr0P3kk09q0qRJjsdHjx5V+/bt9eOPPyotLU19+vTRzJkzLSkSAAAAAICiKNeh+4cfftAdd9zhePzRRx+pYsWK2rRpkxYsWKDRo0dr8uTJlhQJAAAAAEBRlOvQnZSUpNDQUMfjpUuX6u6775aHx5+Xhd9xxx3avXt3vhcIAAAAAEBRlevQXa5cOZ06dcrxeN26dYqIiHA8ttlsSktLy9fiAAAAAAAoynIdum+44QZNmjRJdrtdn3/+uc6ePatOnTo5nt+1a5dCQkIsKRIAAAAAgKIo17cMe+WVV3TzzTfrv//9r9LT0/Xiiy+qQoUKjudnz56tjh07WlIkAAAAAABFUa5Dd5MmTbR9+3atWrVKgYGBTqeWS9L999+vhg0b5nuBAAAAAAAUVbkO3ZJUqVIl3XnnnY7Hv/32m4KCguTm5qbbb78934sDAAAAAKAoy/U13Tlp2LChfv3113wqBQAAAACA4uWaQrcxJr/qAAAAAACg2Lmm0A0AAAAAAC7vmkL3iy++qIoVK+ZXLQAAAAAAFCt5mkjt74YMGZJfdQAAAAAAUOzk2+nlhw4dUt++ffNrcwAAAAAAFHn5FrpPnjypGTNm5NfmAAAAAAAo8nJ9evmXX355xef37dt3zcUAAAAAAFCc5Dp0d+vWTTab7Yq3CbPZbPlSFAAAAAAAxUGuTy+vWrWq5s2bJ7vdnuPXhg0brKwTAAAAAIAiJ9ehu3nz5lq/fv1ln/+no+AAAAAAAJQ0uT69/LnnnlNqaupln69Tp46+++67fCkKAAAAAIDiINehu3379ld8vkyZMurYseM1FwQAAAAAQHGR69PL9+3bx+njAAAAAADkQa5Dd926dXXs2DHH4x49eig5OdmSogAAAAAAKA5yHbr/fpR70aJFV7zGGwAAAACAki7XoRsAAAAAAORNrkO3zWaTzWbLNgYAAAAAAHKW69nLjTHq06ePvLy8JEkXLlxQ//79VaZMGafl5s2bl78VAgAAAABQROU6dPfu3dvp8b///e98LwYAAAAAgOIk16H7gw8+sLIOAAAAAACKHSZSAwAAAADAIoRuAAAAAAAsQugGAAAAAMAihG4AAAAAACxC6AYAAAAAwCKEbgAAAAAALELoBgAAAADAIoRuAAAAAAAsQugGAAAAAMAihG4AAAAAACxC6AYAAAAAwCKEbgAAAAAALELoBgAAAADAIoRuAAAAAAAsQugGAAAAAMAihG4AAAAAACxC6AYAAAAAwCKEbgAAAAAALELoBgAAAADAIoRuAAAAAAAsQugGAAAAAMAihG4AAAAAACxC6AYAAAAAwCKEbgAAAAAALELoBgAAAADAIoRuAAAAAAAsQugGAAAAAMAihG4AAAAAACxC6AYAAAAAwCKEbgAAAAAALELoBgAAAADAIoRuAAAAAAAsQugGAAAAAMAihG4AAAAAACxC6AYAAAAAwCKEbgAAAAAALELoBgAAAADAIoRuAAAAAAAsQugGAAAAAMAihG4AAAAAACxC6AYAAAAAwCKEbgAAAAAALELoBgAAAADAIoRuAAAAAAAsQugGAAAAAMAihG4AAAAAACxC6AYAAAAAwCKEbgAAAAAALFIoQvfkyZMVGhoqb29vRUREaN26dblab/bs2bLZbOrWrZu1BQIAAAAAcBVcHrrnzJmj2NhYxcXFacOGDQoLC1NUVJSOHj16xfV+/fVXPfvss2rfvn0BVQoAAAAAQN64PHSPHz9e/fr1U3R0tBo2bKgpU6aodOnSmj59+mXXycjIUM+ePTVixAjVqlWrAKsFAAAAACD3XBq6L168qPXr1ysyMtIx5ubmpsjISK1Zs+ay640cOVJVqlTRww8//I+vkZaWpjNnzjh9AQAAAABQEFwauo8fP66MjAwFBAQ4jQcEBCgpKSnHdVauXKlp06Zp6tSpuXqN+Ph4lS9f3vEVEhJyzXUDAAAAAJAbLj+9PC/Onj2rhx56SFOnTlWlSpVytc6QIUN0+vRpx9ehQ4csrhIAAAAAgD95uPLFK1WqJHd3dyUnJzuNJycnKzAwMNvye/fu1a+//qquXbs6xux2uyTJw8NDO3fuVO3atZ3W8fLykpeXlwXVAwAAAABwZS490u3p6anmzZsrMTHRMWa325WYmKjWrVtnW75+/frasmWLNm3a5Pi64447dNNNN2nTpk2cOg4AAAAAKFRceqRbkmJjY9W7d2+1aNFCrVq10oQJE5Samqro6GhJUq9evRQcHKz4+Hh5e3vr+uuvd1rfz89PkrKNAwAAAADgai4P3T169NCxY8c0bNgwJSUlKTw8XAkJCY7J1Q4ePCg3tyJ16TkAAAAAAJIKQeiWpJiYGMXExOT43LJly6647ocffpj/BQEAAAAAkA84hAwAAAAAgEUI3QAAAAAAWITQDQAAAACARQjdAAAAAABYhNANAAAAAIBFCN0AAAAAAFiE0A0AAAAAgEUI3QAAAAAAWITQDQAAAACARQjdAAAAAABYhNANAAAAAIBFCN0AAAAAAFiE0A0AAAAAgEUI3QAAAAAAWITQDQAAAACARQjdAAAAAABYhNANAAAAAIBFCN0AAAAAAFiE0A0AAAAAgEUI3QAAAAAAWITQDQAAAACARQjdAAAAAABYhNANAAAAAIBFCN0AAAAAAFiE0A0AAAAAgEUI3QAAAAAAWITQDQAAAACARQjdAAAAAABYhNANAAAAAIBFCN0AAAAAAFiE0A0AAAAAgEUI3QAAAAAAWITQDQAAAACARQjdAAAAAABYhNANAAAAAIBFCN0AAAAAAFiE0A0AAAAAgEUI3QAAAAAAWITQDQAAAACARQjdAAAAAABYhNANAAAAAIBFCN0AAAAAAFiE0A0AAAAAgEUI3QAAAAAAWITQDQAAAACARQjdAAAAAABYhNANAAAAAIBFCN0AAAAAAFiE0A0AAAAAgEUI3QAAAAAAWITQDQAAAACARQjdAAAAAABYhNANAAAAAIBFCN0AAAAAAFiE0A0AAAAAgEUI3QAAAAAAWITQDQAAAACARQjdAAAAAABYhNANAAAAAIBFCN0AAAAAAFiE0A0AAAAAgEUI3QAAAAAAWITQDQAAAACARQjdAAAAAABYhNANAAAAAIBFCN0AAAAAAFiE0A0AAAAAgEUI3QAAAAAAWITQDQAAAACARQjdAAAAAABYhNANAAAAAIBFCN0AAAAAAFiE0A0AAAAAgEUI3QAAAAAAWITQDQAAAACARQjdAAAAAABYhNANAAAAAIBFCN0AAAAAAFiE0A0AAAAAgEUI3QAAAAAAWITQDQAAAACARQjdAAAAAABYhNANAAAAAIBFCN0AAAAAAFiE0A0AAAAAgEUI3QAAAAAAWITQDQAAAACARQjdAAAAAABYhNANAAAAAIBFCN0AAAAAAFiE0A0AAAAAgEUI3QAAAAAAWITQDQAAAACARQjdAAAAAABYhNANAAAAAIBFCN0AAAAAAFiE0A0AAAAAgEUI3QAAAAAAWITQDQAAAACARQjdAAAAAABYhNANAAAAAIBFCN0AAAAAAFiE0A0AAAAAgEUI3QAAAAAAWITQDQAAAACARQjdAAAAAABYhNANAAAAAIBFCkXonjx5skJDQ+Xt7a2IiAitW7fusstOnTpV7du3V4UKFVShQgVFRkZecXkAAAAAAFzF5aF7zpw5io2NVVxcnDZs2KCwsDBFRUXp6NGjOS6/bNkyPfDAA/ruu++0Zs0ahYSE6NZbb9Xhw4cLuHIAAAAAAK7M5aF7/Pjx6tevn6Kjo9WwYUNNmTJFpUuX1vTp03Nc/uOPP9YTTzyh8PBw1a9fX++//77sdrsSExMLuHIAAAAAAK7MpaH74sWLWr9+vSIjIx1jbm5uioyM1Jo1a3K1jXPnzunSpUuqWLFijs+npaXpzJkzTl8AAAAAABQEl4bu48ePKyMjQwEBAU7jAQEBSkpKytU2XnjhBQUFBTkF97+Kj49X+fLlHV8hISHXXDcAAAAAALnh8tPLr8WYMWM0e/ZsffHFF/L29s5xmSFDhuj06dOOr0OHDhVwlQAAAACAksrDlS9eqVIlubu7Kzk52Wk8OTlZgYGBV1z39ddf15gxY/Ttt9+qSZMml13Oy8tLXl5e+VIvAAAAAAB54dIj3Z6enmrevLnTJGiZk6K1bt36suv95z//0SuvvKKEhAS1aNGiIEoFAAAAACDPXHqkW5JiY2PVu3dvtWjRQq1atdKECROUmpqq6OhoSVKvXr0UHBys+Ph4SdLYsWM1bNgwzZo1S6GhoY5rv319feXr6+uy/QAAAAAA4O9cHrp79OihY8eOadiwYUpKSlJ4eLgSEhIck6sdPHhQbm5ZB+TfeecdXbx4Uffee6/TduLi4jR8+PCCLB0AAAAAgCtyeeiWpJiYGMXExOT43LJly5we//rrr9YXBAAAAABAPijSs5cDAAAAAFCYEboBAAAAALAIoRsAAAAAAIsQugEAAAAAsAihGwAAAAAAixC6AQAAAACwCKEbAAAAAACLELoBAAAAALAIoRsAAAAAAIsQugEAAAAAsAihGwAAAAAAixC6AQAAAACwCKEbAAAAAACLELoBAAAAALAIoRsAAAAAAIsQugEAAAAAsAihGwAAAAAAixC6AQAAAACwCKEbAAAAAACLELoBAAAAALAIoRsAAAAAAIsQugEAAAAAsAihGwAAAAAAixC6AQAAAACwCKEbAAAAAACLELoBAAAAALAIoRsAAAAAAIsQugEAAAAAsAihGwAAAAAAixC6AQAAAACwCKEbAAAAAACLELoBAAAAALAIoRsAAAAAAIsQugEAAAAAsAihGwAAAAAAixC6AQAAAACwCKEbAAAAAACLELoBAAAAALAIoRsAAAAAAIsQugEAAAAAsAihGwAAAAAAixC6AQAAAACwCKEbAAAAAACLELoBAAAAALAIoRsAAAAAAIsQugEAAAAAsAihGwAAAAAAixC6AQAAAACwCKEbAAAAAACLELoBAAAAALAIoRsAAAAAAIsQugEAAAAAsAihGwAAAAAAixC6AQAAAACwCKEbAAAAAACLELoBAAAAALAIoRsAAAAAAIsQugEAAAAAsAihGwAAAAAAixC6AQAAAACwCKEbAAAAAACLELoBAAAAALAIoRsAAAAAAIsQugEAAAAAsAihGwAAAAAAixC6AQAAAACwCKEbAAAAAACLELoBAAAAALAIoRsAAAAAAIsQugEAAAAAsAihGwAAAAAAixC6AQAAAACwCKEbAAAAAACLELoBAAAAALAIoRsAAAAAAIsQugEAAAAAsAihGwAAAAAAixC6AQAAAACwCKEbAAAAAACLELoBAAAAALAIoRsAAAAAAIsQugEAAAAAsAihGwAAAAAAixC6AQAAAACwCKEbAAAAAACLELoBAAAAALAIoRsAAAAAAIsQugEAAAAAsAihGwAAAAAAixC6AQAAAACwCKEbAAAAAACLELoBAAAAALAIoRsAAAAAAIsQugEAAAAAsAihGwAAAAAAixC6AQAAAACwCKEbAAAAAACLELoBAAAAALAIoRsAAAAAAIsQugEAAAAAsAihGwAAAAAAixC6AQAAAACwCKEbAAAAAACLELoBAAAAALAIoRsAAAAAAIsQugEAAAAAsAihGwAAAAAAixSK0D158mSFhobK29tbERERWrdu3RWX/+yzz1S/fn15e3urcePGWrRoUQFVCgAAAABA7rk8dM+ZM0exsbGKi4vThg0bFBYWpqioKB09ejTH5VevXq0HHnhADz/8sDZu3Khu3bqpW7du+uWXXwq4cgAAAAAArszloXv8+PHq16+foqOj1bBhQ02ZMkWlS5fW9OnTc1x+4sSJ6ty5s5577jk1aNBAr7zyipo1a6a33nqrgCsHAAAAAODKXBq6L168qPXr1ysyMtIx5ubmpsjISK1ZsybHddasWeO0vCRFRUVddnkAAAAAAFzFw5Uvfvz4cWVkZCggIMBpPCAgQDt27MhxnaSkpByXT0pKynH5tLQ0paWlOR6fPn1aknTmzJlrKb1gXCi4lyr0/aAXzuhHFnqRhV44ox9Z6IWzAuoHvXBW6PtBL5zRjyz0Igu9cJJZozHmisu5NHQXhPj4eI0YMSLbeEhIiAuqKbzKjynv6hIKDXrhjH5koRdZ6IUz+pGFXmShF87oRxZ64Yx+ZKEXWYpSL86ePavy5S9fr0tDd6VKleTu7q7k5GSn8eTkZAUGBua4TmBgYJ6WHzJkiGJjYx2P7Xa7Tp48KX9/f9lstmvcg8LnzJkzCgkJ0aFDh1SuXDlXl+NS9CILvXBGP7LQiyz0whn9yEIvstALZ/QjC73IQi+cFed+GGN09uxZBQUFXXE5l4ZuT09PNW/eXImJierWrZukP0NxYmKiYmJiclyndevWSkxM1FNPPeUYW7JkiVq3bp3j8l5eXvLy8nIa8/Pzy4/yC7Vy5coVuzf11aIXWeiFM/qRhV5koRfO6EcWepGFXjijH1noRRZ64ay49uNKR7gzufz08tjYWPXu3VstWrRQq1atNGHCBKWmpio6OlqS1KtXLwUHBys+Pl6SNGjQIHXs2FHjxo3T7bffrtmzZ+unn37Se++958rdAAAAAAAgG5eH7h49eujYsWMaNmyYkpKSFB4eroSEBMdkaQcPHpSbW9Yk623atNGsWbM0dOhQvfjii6pbt67mz5+v66+/3lW7AAAAAABAjlweuiUpJibmsqeTL1u2LNvYfffdp/vuu8/iqoomLy8vxcXFZTulviSiF1nohTP6kYVeZKEXzuhHFnqRhV44ox9Z6EUWeuGMfkg280/zmwMAAAAAgKvi9s+LAAAAAACAq0HoBgAAAADAIoRuAAAAAAAsQugGAAAAAMAihG4AAEqYjIwMV5cAAECJQegGiqjU1FRXl1BoLFy4UM2aNdPKlSslSXa73cUVudbs2bM1f/58cXMK6dtvv9Wbb76p/fv3S1KJ78n//vc/VapUSbNmzXJ1KS536NAhpaWl6eLFi5JK9nujJO87/hnvD+DaFYr7dCNn27Zt04IFC3TPPffouuuuc3U5LrVjxw5NmTJFdrtd5cuXV69evVS3bl1Xl+US27Zt09ixY3Xq1Cl5enqqa9eu6tWrl6vLcplff/1VMTExOnDggMaPH6927drJza1kfp64ZcsWPfHEE1q1apWGDx+uLl26yNPT09VlucSuXbv02GOPacOGDYqIiJCvr69q1qwpm83m6tJcYsuWLXrkkUf0448/SpKWL1+uhx56yMVVucbWrVs1ZMgQHTx4UJcuXVJ4eLhmzJghD4+S9yfR1q1b9dprr8lms6latWrq27evatas6eqyXGbOnDn67rvvVLNmTbVt21bt2rWT9GfoLIm/Oz777DN16tRJ/v7+ysjIkLu7u6tLcpmvv/5aO3bskJ+fn8LCwtSsWTNXl+Qy8+fP19KlS1WmTBk1bdpUd955p7y8vErsz0muGRQ6ly5dMhMnTjQ2m83YbDbz7rvvmosXL7q6LJdIS0szzz77rPHx8TEtWrQwdevWNTabzdSqVct88cUXri6vQJ0/f948/fTTxsvLy7Ru3dp07drVVKtWzZQqVcq8/fbbri7PZU6fPm38/PxMo0aNTLVq1czMmTONMcZkZGS4uLKCk5KSYh555BFjs9lMx44dzfz5883Ro0ddXZbL7N+/3zRt2tS0bdvWfPLJJ2bz5s2uLsllzp49a/r06WNsNpu58cYbzZdffmnq1Kljunbtai5cuODq8gqM3W43drvdTJw40VSsWNG0bdvWPP300yYyMtLYbDbzwgsvGGNKzu+NjIwMM2rUKOPl5WXCw8NNWFiYsdlsJigoyEybNs3V5RW4pKQkExUVZcqUKWOaNWtmKlSoYLy8vExcXJz5448/jDF/vodKir1795p//etfxmazmXvuuccYU7L2/682b95s2rRpY/z8/EzNmjWNzWYzlSpVMp9++mmJ+9t848aNplWrVsbPz8+0aNHCVK5c2fH7MyUlxdXlFXqE7kLm0qVLZty4caZcuXKmXbt2pmHDhqZx48Yl8o/Gs2fPmhdffNHUqlXLjB071uzcudNkZGSYxMREExQUZNq1a2fOnz/v6jILxIkTJ0x0dLSpW7euee2118zOnTuNMcYcOXLEhIeHm/Dw8BL1B3Qmu91uDh06ZG688UYzatQoU69ePdOyZUuTmprqeL6427t3rylfvrzx8PAwkyZNMgcOHDCXLl1ydVkuNWrUKFOlShWzdOlSk5aW5upyXGbu3LnGx8fH1KxZ07z99ttm//795tKlS6ZPnz4mKCjIpKenu7rEAvX777+bevXqmUceecTs3r3bGGPMhQsXTPfu3U3jxo1L1Htlx44dplq1aubxxx939GLt2rWmXr16xtPT0yQmJpaYDyCMMWbGjBmmYsWK5uOPPza///67OXHihOnTp48pW7aseeKJJ1xdXoHaunWriYyMNH5+fo4PY1atWmWMMSXu35Zly5aZZs2amZtuusl8/vnnZtu2bWbJkiWmadOmpn79+uann35ydYkFwm63m3nz5pnrrrvO3HzzzWb+/Plm3759JiMjwzzwwAMmMDDQ/Pzzz64us9AjdBdCAwcONA0bNjQHDhwwq1evNh4eHuall14yZ8+eNcaUjCBhzJ9Hq2rWrGkee+wxc+rUKafnHnvsMVOlShXz448/uqi6gnXq1Cnz+OOPm1dffdXxPjDmz38Amzdvbjp16mTOnTtXov5IynT06FHj7e1ttm/fbsaMGWN8fX3NqFGjjDEl46jV6dOnzR133GFCQkLMmjVrHOPr1q0zK1euNLt27XJhdQUvPT3ddO7c2dx7772Osf/973+mf//+ZuLEiWb58uUurK5grV692owZM8b8/PPPTkdkBg4caEqVKmVWrFjhwuoK3jvvvGNsNpvZtGmT03inTp1MdHS0i6pyjTFjxhibzWYOHz7sNJ7Zo5tvvjlbn4qzjh07mhtuuMFpLDU11XGWyMKFC40xJePvr48//tj4+fmZ119/3axYscLUqVPHNG3a1NVlFbj09HRz2223maCgILN8+XKnDymXL19u3NzczKeffmqMKf7vC7vdbgYPHmzuuece88MPPzjt76RJk4zNZjPr1683xpSMv7uuFqG7EDp9+rTjh/v8+fOmV69epnLlymbZsmUurqxg2e1289577zmNZf7h+OmnnxoPDw/HEd+SIDk5OdvYvHnzTGhoqHn00UddUJHrZWRkmMOHD5t69eqZFStWmKSkJHPDDTeYmjVrOo7eFPd/DI0xZtWqVaZ06dJmxIgRZvXq1aZt27bGzc3NcYnKY489Zn799VdXl1lgqlevbmJjY01aWpq58cYbjc1mMzVr1jTu7u7G29vbzJw5s8ScGfLX/cz8d2XevHnGZrOZBQsWuKosl/j000+NzWYz3333nWNs8eLF5vrrrzdDhw41y5cvNydPnnRdgQVo2LBhxtfX1+zbt88Yk/U+OXXqlPH19TU2m83ExcWZM2fOuLJMy9ntdnPx4kUTFRVl2rVr5xjPPKK7fv1607x5c1O7du1i/29JZljat2+f0wcuI0aMMDabzXHZQUk42p35u3LatGnms88+c4xnvge2b99uypQpY1588UWX1FeQMnuxbdu2bH+HpqSkmK5du5oOHTqUuA/4rwahuxDL/OH+5ZdfTMWKFc1DDz2UY/AqzjJ/uf/9NMjXXnvNeHh4lNgf8tOnT5uHHnrIcW1R27ZtTZcuXUz//v1dXVqBO3nypCldurTj9Ld3333XVKxY0TzyyCPGmD9PzS/ufyxdunTJDBo0yNhsNlO+fHnTqVMnM23aNPPxxx87rvW+8cYbXV1mgbDb7aZLly6madOmJiEhwTRq1MgsWLDA/P7772bp0qUmMjLSVKxY0cybN8/VpbrMhg0bjI+Pj3n11VeNMSXnyMThw4dNs2bNTKlSpcz9999v7rjjDuPu7m5q1aplgoODja+vr2nXrl2JmA/hxx9/NDabzUyYMMExlpGRYbZu3Wpq165tQkNDTXBwcLH6YHvr1q1m9OjROe7T3XffberVq2e2bNlijHH+mXjvvfeMzWYzb7zxRrbnirK/9+Pv/05m/v21Y8cO06ZNG+Pv7+/Y9+LSg0yX60XmXBB///+DBw8aLy8v88477zgtXxz80/si83ufkJBgmjdvbmw2mwkICDC9e/c2o0ePdvxdXtzeI/mB0F1EjBw50nh6eppPP/3U6RdASZP5Qzxo0CATGBhY4iaxyPTll1+arl27mhkzZphffvnFnDx50jz77LPGw8PDDBkypES9N/bt22euu+46M3/+fGPMn5Pv3XXXXaZSpUqmR48epkWLFuaXX35xcZXW2717t2nVqpV5+OGHze+//+54D2RkZJhhw4YZm81mPvjgA9cWWUBeffVV4+XlZcqUKWPuv/9+p+d+/fVXU61aNXP77beb33//3UUVutaePXtM1apVTdeuXUvEUau/2rBhg7n//vtNw4YNjbe3t3n33XfNwYMHzenTp83UqVNNmTJlzKOPPmpOnz7t6lItd8MNN5gaNWqYRYsWmUuXLpnz58+bhx9+2ISFhZm3337bMZGrMUX7D+grTU6b+d/58+cbm81mpk+f7tjXzA/7f/31V3PzzTebmjVrFovr/q9mst7333/f2Gw288wzzxhjsh8IKaquduLiJUuWGE9Pz2L14W1ee1G9enXTuXNns3DhQrN69WozadIkU7p0aXPzzTeXmDPJ8orQXUQcOXLENGjQwHTs2NFx2mxJ1rx5c3P33XcbY4rPL/+8SE9Pz3ad+9GjR83DDz9sypcvX2JOkTTmzyPZXl5eZuXKlY6x5557znh6ehp3d3fz0ksvmWPHjrmwwoKRnp5uli5dag4cOOAYywxU27ZtM40bNzZNmjRxVXkF6ujRo6ZixYqmVKlSZsSIEcYY56MUr7/+uvH09CzRv0vbtm1rIiIizOnTp0vUh3TG/PlemDVrlhk0aJBj0kVj/py8c/DgwaZ06dJm7969LqywYBw5csQxG/P1119vKlWqZCpWrGhWrFhhDh06ZKpWrWq6devm6jKvSW4npz137pxp2rSpad++fY6X4owYMcL4+fk5ru0uqvI6WW/m74bk5GRz7733GpvN5uhPUf/A7momLs7sxwcffGC8vLzMoUOHCqpcS+WlF5nf999++y3bh5MvvfSSKVWqlJk8eXKB1F3UlMyb2RZBgYGBevHFF7VixQotWrRIkmS327Vv3z4XV1bwjh07pm3btql169aSJHd3d9ntdp06dcq1hRUgd3d3lS9fXtKf9w+12+2qXLmy6tWrpzNnzmjdunUurrDgnDp1SjVq1NC5c+e0Zs0adejQQePHj1eNGjVUpkwZhYSEqFKlSrLb7a4u1VLu7u666aabVL16dcdY5n2HGzRooOrVqyslJUVHjx51VYkFpnLlynruueeUnp6urVu36tKlS073Dq1bt64uXbqkTZs2ua5IFzHGSJJuuOEGbd68Wenp6bLZbI7xksBms2nFihU6deqUSpcurfT0dNntdvn6+qpJkyY6f/68Vq1a5eoyLRcYGKhvvvlGo0aNUp06dTRo0CB9//33at++vapVqyY/Pz95e3tLUpH9/enh4aEDBw6oWrVq+vjjj/X+++9r+/bt+vTTT5WSkuJYzsfHR88884xWrlypefPm6dy5c5KkixcvSpLuuOMOnTlzxvG4qP68XK4fc+bMcfTjr/uW+XuzSpUq6tOnjypUqKABAwY4tnX69GlduHCh4HckH+S1F1JWP1asWKHg4GAFBwc7lrl06ZIyMjIKdifySW5+TjL3M/PviuDgYJUrV05S1s/JQw89JJvNpkOHDrlgL4oA1+V95NXZs2dNly5dTN26dc3nn39uRo0aZWrWrFliZvDOtHTpUmOz2RwTyx05csTMnDnTtGzZskRci/dXfz/lr3///sbX19ds3brVRRUVvN9++81xr1kPDw/TunVr880335hVq1aZRo0ameDg4BJ15D/T349eRkREmPr16xeL0yNz48KFC6Z+/fqOCcP+ekbMf/7zH+Pp6WnWrVvnwgpda+zYscZms5m5c+e6upQCZ7fbTe/evU2LFi1MUlKS03OjRo0yHh4eZsmSJS6qrnA4deqUqVmzpnn44YddXco1+/vktL17985xctozZ86Yu+++2wQGBjouV8q0bt06Y7PZzIwZMwqsbqvkth+ZMv8tSU1NNbGxscZms5n//e9/ZuPGjeaxxx4zcXFxBVV6vstrL4z5c/KwRo0ame7duxtj/rykbfv27eaNN94w06dPL5C6rXA1vTDG+e/Qn3/+2dhsNvPKK69YWmtRReguYr744gvj7e1t/Pz8jM1mM23bti0xQTPzF//YsWONn5+f2bVrl/nuu+/MXXfdZUqVKmVatGhhjh8/XuJOlTTmz8nEZs6caapUqWIGDRpUonqQnp5uHnroIVOnTh3z5ptvmoMHDzr+4Xj55ZdNr169SuQptJlOnjzpuNZqypQpri6nQK1du9bUqFHDBAYGmkmTJplt27aZ+fPnm7CwMNO1a1eTkpLi6hILXOYfSOvXr3eakbik/Xz873//Mz4+PubJJ580xvz54d3//vc/U7duXXPrrbeaP/74w7UFFqC/f+9PnDhhRo8ebfz9/Z0u2ynqcjM57aFDh0xAQIBp1KiRSUhIMMb8OQFfTEyMqVGjRrYPaYqyvEzWm7nsDz/8YOrWrWv8/f1NcHCw8fDwMJMmTSqwmq2Sl14cPnzYlClTxkyaNMmkpKSYDz74wDRs2NC4ubkV6dCdKbe9+OvvDbvdbnbt2mXuvfdeU716dbN///6CKrdIIXQXEampqearr75yXFPTtGlTs3TpUleX5RJ33323qV27tunXr58pW7asqVu3rvnmm29cXZZL7Nixw0yfPt3079/flC1b1nTq1Mns2bPH1WUVuN9++8388ssv2SbvKOrXnF2LXbt2mY8//tg88cQTpmzZsuaOO+4okROHff/996ZBgwbGZrMZf39/4+PjYxo3bmw2btzo6tJc6sCBA6ZcuXKmZ8+eri7FJex2u/nXv/5lbDabqVKlimnevLnx9/c3YWFhJfa98euvv5pp06aZ2NhY4+fnZ/r162fOnj1bLD+QyWly2sx/LxYvXmyaNWtmbDabCQ8PN61bt3bMD5Genl5i+vH3/Tx48KDj7iA2m838+9//LpYHff6pF998842x2WzmtddeM7fddpux2Wzm3nvvLZZ3F8rN++KXX34xM2bMMDfffLMpX768GTdunMnIyCiWPyfXitBdROzdu9cEBgaacuXKmbfeesvV5bjM+fPnTXh4uLHZbKZcuXKOW3iUREeOHDHNmjUz1apVM7Vq1TJvvvmmq0tCIZF57/KqVauakJCQYnEk4locOHDAfPHFF+Y///mP+fTTT11dTqGQlJRk2rRpUyxOl71ap0+fNhMnTjS9evUy9913n/nwww9dXZLLXLp0yYwfP95UqVLF1KhRo9j/e5I5Oe2NN96Y44SKx48fN/Hx8aZfv36mR48eZvXq1S6osuD8Uz/OnTtnhgwZYmw2m2nZsmWxvjTnn3qR2QebzWYaNmxYrM4G+bsr9cJut5t7773X+Pv7m4CAANOyZUuzZs0aF1VaNBC6i5CPPvrInD9/3tVluNzzzz9vXnjhBW5JYIxZsWKFmT9/fom5The5t2DBAjNz5kzeG8imuN5r91pwVMaYP/74wyxevLjE/M6YOXOmsdlsjg8lMzIySuSZYply6kfmLP7p6elmwYIFJea2kzn1IjN0zp4925QvX97MmjXLlSUWmCv9nPzwww/mpZdeKvKz+hcUmzFFdApGlFh2u11ubky8DwAArk5KSop69Oih3bt3Kz4+Xjt37tT777+vTz/9VC1atHB1eQXucv2YM2eOWrZs6eryChS9yHK5XsyePVutWrVydXlFCqEbAAAAJc78+fP1wAMPyNvbW6dPn1abNm30xRdfqHLlyq4uzSXoRxZ6kYVe5A8OFwIAAKDEOHfunBYuXKiPP/5YaWlpqlmzphITE7Vy5coSGSToRxZ6kYVe5C+OdAMAAKDE2Ldvn9q2batz585p9OjRGjBggKtLcin6kYVeZKEX+YvQDQAAgBJl5syZuu++++Tt7e3qUgoF+pGFXmShF/mH0A0AAAAAgEW4phsAAAAAAIsQugEAAAAAsAihGwAAAAAAixC6AQAAAACwCKEbAAAAAACLELoBAAAAALAIoRsAAAAAAIsQugEAAAAAsAihGwCQo19//VU2m02bNm1ydSkOO3bs0A033CBvb2+Fh4fnuIwxRo8++qgqVqxY6OovrJYtWyabzaZTp065upRsrK6tML7PC4vc9CY0NFQTJkzI03ZXrVqlxo0bq1SpUurWrds11QgARQGhGwAKqT59+shms2nMmDFO4/Pnz5fNZnNRVa4VFxenMmXKaOfOnUpMTMxxmYSEBH344Yf66quvdOTIEV1//fX58tp9+vQhIORSYQ7xfxcSEpKv7xNXsNlsmj9/vkte+8cff9Sjjz6ap3ViY2MVHh6u/fv368MPP7SmMAAoRAjdAFCIeXt7a+zYsfrjjz9cXUq+uXjx4lWvu3fvXrVr1041atSQv7//ZZepWrWq2rRpo8DAQHl4eFz161khIyNDdrvd1WXg/7m7uxfK90lRUblyZZUuXTpP6+zdu1edOnVStWrV5OfnZ01hAFCIELoBoBCLjIxUYGCg4uPjL7vM8OHDs51qPWHCBIWGhjoeZx6lHT16tAICAuTn56eRI0cqPT1dzz33nCpWrKhq1arpgw8+yLb9HTt2qE2bNvL29tb111+v5cuXOz3/yy+/6LbbbpOvr68CAgL00EMP6fjx447nb7zxRsXExOipp55SpUqVFBUVleN+2O12jRw5UtWqVZOXl5fCw8OVkJDgeN5ms2n9+vUaOXKkbDabhg8fnm0bffr00ZNPPqmDBw/KZrM5emC32xUfH6+aNWvKx8dHYWFh+vzzzx3rZWRk6OGHH3Y8X69ePU2cONGpxzNmzNCCBQtks9lks9m0bNmyHI/obtq0STabTb/++qsk6cMPP5Sfn5++/PJLNWzYUF5eXjp48KDS0tL07LPPKjg4WGXKlFFERISWLVvm2M6BAwfUtWtXVahQQWXKlFGjRo20aNGiHHsnSW+//bbq1q0rb29vBQQE6N5773Xq7ZX2PycrV65U+/bt5ePjo5CQEA0cOFCpqamO59PS0vTCCy8oJCREXl5eqlOnjqZNm6Zff/1VN910kySpQoUKstls6tOnT67rWLRoka677jr5+PjopptucvTxcnI6BfrUqVOO75Ek/fHHH+rZs6cqV64sHx8f1a1b1/Fe//v6md/TxMREtWjRQqVLl1abNm20c+dOp9d99dVXVaVKFZUtW1aPPPKIBg8efNlLHv663cWLF6tp06by8fFRp06ddPToUX399ddq0KCBypUrpwcffFDnzp1zrJfT6dvh4eGO93/me/yuu+5yes/ndGbGU089pRtvvNHxOCEhQe3atZOfn5/8/f31r3/9S3v37r3sPuTk7/XZbDa9//77uuuuu1S6dGnVrVtXX375paSsXp84cUJ9+/aVzWZzHOlevny5WrVqJS8vL1WtWlWDBw9Wenp6nmoBgMKK0A0AhZi7u7tGjx6tN998U7/99ts1bWvp0qX6/ffftWLFCo0fP15xcXH617/+pQoVKmjt2rXq37+/HnvssWyv89xzz+mZZ57Rxo0b1bp1a3Xt2lUnTpyQ9Ge46dSpk5o2baqffvpJCQkJSk5OVvfu3Z22MWPGDHl6emrVqlWaMmVKjvVNnDhR48aN0+uvv67NmzcrKipKd9xxh3bv3i1JOnLkiBo1aqRnnnlGR44c0bPPPpvjNjKD+5EjR/Tjjz9KkuLj4/XRRx9pypQp2rp1q55++mn9+9//dnyAYLfbVa1aNX322Wfatm2bhg0bphdffFGffvqpJOnZZ59V9+7d1blzZx05ckRHjhxRmzZtct37c+fOaezYsXr//fe1detWValSRTExMVqzZo1mz56tzZs367777lPnzp0d+ztgwAClpaVpxYoV2rJli8aOHStfX98ct//TTz9p4MCBGjlypHbu3KmEhAR16NDB8fw/7f/f7d27V507d9Y999yjzZs3a86cOVq5cqViYmIcy/Tq1UuffPKJJk2apO3bt+vdd9+Vr6+vQkJCNHfuXEnSzp07deTIEccHGP9Ux6FDh3T33Xera9eu2rRpkyPMXquXX35Z27Zt09dff63t27frnXfeUaVKla64zksvvaRx48bpp59+koeHh/r27et47uOPP9aoUaM0duxYrV+/XtWrV9c777yTq1qGDx+ut956S6tXr9ahQ4fUvXt3TZgwQbNmzdLChQv1zTff6M0338z1vmW+xz/44AOn93xupKamKjY2Vj/99JMSExPl5uamu+6665rPxBgxYoS6d++uzZs3q0uXLurZs6dOnjzpOJW/XLlymjBhgo4cOaIePXro8OHD6tKli1q2bKmff/5Z77zzjqZNm6ZXX331muoAgELDAAAKpd69e5s777zTGGPMDTfcYPr27WuMMeaLL74wf/31HRcXZ8LCwpzWfeONN0yNGjWctlWjRg2TkZHhGKtXr55p376943F6eropU6aM+eSTT4wxxuzfv99IMmPGjHEsc+nSJVOtWjUzduxYY4wxr7zyirn11ludXvvQoUNGktm5c6cxxpiOHTuapk2b/uP+BgUFmVGjRjmNtWzZ0jzxxBOOx2FhYSYuLu6K2/n7vl+4cMGULl3arF692mm5hx9+2DzwwAOX3c6AAQPMPffc43j81+9Hpu+++85IMn/88YdjbOPGjUaS2b9/vzHGmA8++MBIMps2bXIsc+DAAePu7m4OHz7stL2bb77ZDBkyxBhjTOPGjc3w4cOvuK+Z5s6da8qVK2fOnDmT7bnc7P/f9+Phhx82jz76qNPy33//vXFzczPnz583O3fuNJLMkiVLcqwnp77kpo4hQ4aYhg0bOj3/wgsvZNvWX2W+Tzdu3OgY++OPP4wk89133xljjOnatauJjo7O1fqZtX/77beOZRYuXGgkmfPnzxtjjImIiDADBgxw2k7btm2z/Rz+VU7bjY+PN5LM3r17HWOPPfaYiYqKcjyuUaOGeeONN5y29fefA0nmiy++cFomp/froEGDTMeOHS9b47Fjx4wks2XLFmNMzr39u7/XJ8kMHTrU8TglJcVIMl9//bVjrHz58uaDDz5wPH7xxRdNvXr1jN1ud4xNnjzZ+Pr6Ov3OAoCiiguYAKAIGDt2rDp16pTj0d3catSokdzcsk5wCggIcJo8yt3dXf7+/jp69KjTeq1bt3b8v4eHh1q0aKHt27dLkn7++Wd99913OR6B3bt3r6677jpJUvPmza9Y25kzZ/T777+rbdu2TuNt27bVzz//nMs9zNmePXt07tw53XLLLU7jFy9eVNOmTR2PJ0+erOnTp+vgwYM6f/68Ll68eMXThfPC09NTTZo0cTzesmWLMjIyHP3JlJaW5rhWfeDAgXr88cf1zTffKDIyUvfcc4/TNv7qlltuUY0aNVSrVi117txZnTt3dpzem9v9/6uff/5Zmzdv1scff+wYM8bIbrdr//792rJli9zd3dWxY8dc9yA3dWzfvl0RERFOz//1/Xe1Hn/8cd1zzz3asGGDbr31VnXr1u0fz1T4a6+rVq0qSTp69KiqV6+unTt36oknnnBavlWrVlq6dOk/1vLX7QYEBKh06dKqVauW09i6detytV/Xavfu3Ro2bJjWrl2r48ePO45wHzx48JomlvvrPpYpU0blypXL9nvlr7Zv367WrVs7TRDZtm1bpaSk6LffflP16tWvuhYAKAwI3QBQBHTo0EFRUVEaMmSI4/rYTG5ubjLGOI1dunQp2zZKlSrl9Nhms+U4lpdTS1NSUtS1a1eNHTs223OZQUX68w9vV0lJSZEkLVy4UMHBwU7PeXl5SZJmz56tZ599VuPGjVPr1q1VtmxZvfbaa1q7du0Vt535IcZf+59T7318fJwCRUpKitzd3bV+/Xq5u7s7LZv5AcYjjzyiqKgoxynH8fHxGjdunJ588sls2y9btqw2bNigZcuW6ZtvvtGwYcM0fPhw/fjjj7na/79LSUnRY489poEDB2Z7rnr16tqzZ0+O613J1dSRG7n5Htx22206cOCAFi1apCVLlujmm2/WgAED9Prrr192u3/92cj83uXHBHh/3+4//Qzm9uf773KzXteuXVWjRg1NnTpVQUFBstvtuv76669pskMp5981TB4IoCQjdANAETFmzBiFh4erXr16TuOVK1dWUlKSjDGOcJCf9xz+4YcfHNcHp6ena/369Y5re5s1a6a5c+cqNDT0mmZ/LleunIKCgrRq1Sqno6erVq1Sq1atrqn+v05edrkjs6tWrVKbNm2cjl7+fUIpT09PZWRkOI1VrlxZ0p/Xm1eoUEFS7nrftGlTZWRk6OjRo2rfvv1llwsJCVH//v3Vv39/DRkyRFOnTs0xdEt/noUQGRmpyMhIxcXFyc/PT0uXLtUtt9zyj/v/d82aNdO2bdtUp06dHJ9v3Lix7Ha7li9frsjIyGzPe3p6SpJTv3LzfWjQoIFj0q1MP/zwwxVr/ev3IPOIeU7fg8qVK6t3797q3bu32rdvr+eee+6KoftK6tWrpx9//FG9evVyjOXlWuq8qFy5so4cOeJ4fObMGe3fv99pmVKlSuX43vzll1+cxjZt2uQIxCdOnNDOnTs1depUx3tw5cqVVuzCP2rQoIHmzp3r9Dts1apVKlu2rKpVq+aSmgAgPzGRGgAUEY0bN1bPnj01adIkp/Ebb7xRx44d03/+8x/t3btXkydP1tdff51vrzt58mR98cUX2rFjhwYMGKA//vjDManUgAEDdPLkST3wwAP68ccftXfvXi1evFjR0dHZQsA/ee655zR27FjNmTNHO3fu1ODBg7Vp0yYNGjTomuovW7asnn32WT399NOaMWOG9u7dqw0bNujNN9/UjBkzJEl169bVTz/9pMWLF2vXrl16+eWXs4Wo0NBQbd68WTt37tTx48d16dIl1alTRyEhIRo+fLh2796thQsXaty4cf9Y03XXXaeePXuqV69emjdvnvbv369169YpPj5eCxculPTnTNOLFy/W/v37tWHDBn333Xdq0KBBjtv76quvNGnSJG3atEkHDhzQRx99JLvdrnr16uVq///uhRde0OrVqxUTE6NNmzZp9+7dWrBggePDltDQUPXu3Vt9+/bV/PnztX//fi1btswx8VyNGjVks9n01Vdf6dixY0pJSclVHf3799fu3bv13HPPaefOnZo1a9Y/3sfZx8dHN9xwg8aMGaPt27dr+fLlGjp0qNMyw4YN04IFC7Rnzx5t3bpVX3311WV7mRtPPvmkpk2bphkzZmj37t169dVXtXnzZqezGfJLp06dNHPmTH3//ffasmWLevfune3siNDQUCUmJiopKclxe8FOnTrpp59+0kcffaTdu3crLi7OKYRXqFBB/v7+eu+997Rnzx4tXbpUsbGx+V5/bjzxxBM6dOiQnnzySe3YsUMLFixQXFycYmNjnS6JAYCiit9kAFCEjBw5Mttpmg0aNNDbb7+tyZMnKywsTOvWrbuma7//bsyYMRozZozCwsK0cuVKffnll46ZnzOPTmdkZOjWW29V48aN9dRTT8nPzy/PfywPHDhQsbGxeuaZZ9S4cWMlJCToyy+/VN26da95H1555RW9/PLLio+PV4MGDdS5c2ctXLhQNWvWlCQ99thjuvvuu9WjRw9FREToxIkT2a7Z7devn+rVq6cWLVqocuXKWrVqlUqVKqVPPvlEO3bsUJMmTTR27Nhcz7j8wQcfqFevXnrmmWdUr149devWTT/++KPj+tWMjAwNGDDAUe91112nt99+O8dt+fn5ad68eerUqZMaNGigKVOm6JNPPlGjRo1ytf9/16RJEy1fvly7du1S+/bt1bRpUw0bNkxBQUGOZd555x3de++9euKJJ1S/fn3169fPcUux4OBgjRgxQoMHD1ZAQIAjrP9THdWrV9fcuXM1f/58hYWFacqUKRo9evQ/9nL69OlKT09X8+bN9dRTT2X7Hnh6emrIkCFq0qSJOnToIHd3d82ePfsft3s5PXv21JAhQ/Tss8+qWbNm2r9/v/r06SNvb++r3ublDBkyRB07dtS//vUv3X777erWrZtq167ttMy4ceO0ZMkShYSEOI72R0VF6eWXX9bzzz+vli1b6uzZs05H5t3c3DR79mytX79e119/vZ5++mm99tpr+V5/bgQHB2vRokVat26dwsLC1L9/fz388MPZPjwBgKLKZv5+wQ8AAADy5JZbblFgYKBmzpzp6lIAAIUM13QDAADkwblz5zRlyhRFRUXJ3d1dn3zyib799lstWbLE1aUBAAohjnQDAADkwfnz59W1a1dt3LhRFy5cUL169TR06FDdfffdri4NAFAIEboBAAAAALAIE6kBAAAAAGARQjcAAAAAABYhdAMAAAAAYBFCNwAAAAAAFiF0AwAAAABgEUI3AAAAAAAWIXQDAAAAAGARQjcAAAAAABYhdAMAAAAAYJH/A4/0NSHxNg64AAAAAElFTkSuQmCC\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Note:\n",
        "plot show that we can use the top 3 (or 4, ..., 7) features to get the best performance - 0.981"
      ],
      "metadata": {
        "id": "D1JMbMzydEPx"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "selector = SelectKBest(mutual_info_classif, k = 3)\n",
        "selector.fit(X_train_v3, y_train_v3)\n",
        "\n",
        "selected_feature_mask = selector.get_support()\n",
        "\n",
        "selected_features = X_train_v3.columns[selected_feature_mask]\n",
        "selected_features\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "fRJC-hgocPoo",
        "outputId": "6fc5f709-cd35-4ee8-e42a-50719178f233"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['flavanoids', 'color_intensity', 'proline'], dtype='object')"
            ]
          },
          "metadata": {},
          "execution_count": 22
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from pprint import pprint\n",
        "results[\"K-best features (3 best)\"] = f1_score_list[2] # correct item from f1_score_list\n",
        "pprint(results)\n",
        "\n",
        "fig, ax = plt.subplots()\n",
        "\n",
        "x = results.keys()\n",
        "y = results.values()\n",
        "\n",
        "ax.bar(x, y, width = 0.4, color='purple')\n",
        "\n",
        "#ax.set_xlabel('Features')\n",
        "ax.set_ylabel('F1-Score weighted')\n",
        "ax.set_ylim(0, 1.2)\n",
        "\n",
        "for idx, value in enumerate(y):\n",
        "  plt.text(x = idx, y = value + 0.01, s = str(round(value, 3)), ha = 'center')\n",
        "plt.tight_layout()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 722
        },
        "id": "XKMnEaI5dz9P",
        "outputId": "454674a6-8b1e-4158-fafe-94c3995529d3"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "{'K-best features (3 best)': 0.981,\n",
            " 'all features': 0.908,\n",
            " 'set variance threshold to 0.3': 0.963,\n",
            " 'set variance threshold to 0.31': 0.963}\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x800 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "3 Recursive feature elimination (RFE)"
      ],
      "metadata": {
        "id": "o2J-Wea4e6Wf"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "X_train_v4, X_test_v4, y_train_v4, y_test_v4 = X_train.copy(), X_test.copy(), y_train.copy(), y_test.copy()"
      ],
      "metadata": {
        "id": "rNdV_YZ9e-XM"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.feature_selection import RFE\n",
        "rfe_f1_score_list = []\n",
        "\n",
        "for k in range(1, 14):\n",
        "  RFE_selector = RFE(estimator=gbc, n_features_to_select = k, step = 1)\n",
        "  RFE_selector.fit(X_train_v4, y_train_v4)\n",
        "\n",
        "  selected_X_train_v4 = RFE_selector.transform(X_train_v4)\n",
        "  selected_X_test_v4 = RFE_selector.transform(X_test_v4)\n",
        "\n",
        "  gbc.fit(selected_X_train_v4, y_train_v4)\n",
        "  RFE_predictions = gbc.predict(selected_X_test_v4)\n",
        "\n",
        "  f1_score_rfe = round(f1_score(y_test_v4, RFE_predictions, average = 'weighted'), 3)\n",
        "\n",
        "  rfe_f1_score_list.append(f1_score_rfe)"
      ],
      "metadata": {
        "id": "Bkz5yFz3epQJ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "fig, ax = plt.subplots()\n",
        "\n",
        "x = np.arange(1, 14)\n",
        "y = rfe_f1_score_list\n",
        "\n",
        "ax.bar(x, y, width = 0.4, color='green')\n",
        "\n",
        "ax.set_xlabel('Number of features selected using RFE')\n",
        "ax.set_ylabel('F1-Score weighted')\n",
        "ax.set_ylim(0, 1.2)\n",
        "ax.set_xticks(np.arange(1, 14))\n",
        "ax.set_xticklabels(np.arange(1, 14), fontsize = 12)\n",
        "\n",
        "for idx, value in enumerate(y):\n",
        "  plt.text(x = idx + 1, y = value + 0.05, s = str(value), ha = 'center')\n",
        "fig.autofmt_xdate()\n",
        "plt.tight_layout()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 650
        },
        "id": "9G6Uz80xhDHI",
        "outputId": "31302aa6-bed3-4da9-c537-539c3281eed5"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x800 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Top 3 features provide the perfect classifier!"
      ],
      "metadata": {
        "id": "0hPcceXiiVKl"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "selector_RFE = RFE(estimator = gbc, n_features_to_select = 3, step = 10)\n",
        "selector_RFE.fit(X_train_v4, y_train_v4)\n",
        "\n",
        "selected_feature_mask = selector_RFE.get_support()\n",
        "\n",
        "selected_features = X_train_v4.columns[selected_feature_mask]\n",
        "selected_features\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ByY0q5BgiCPR",
        "outputId": "6541a729-e2a1-4e9e-856b-5f99c368785e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['color_intensity', 'od280/od315_of_diluted_wines', 'proline'], dtype='object')"
            ]
          },
          "metadata": {},
          "execution_count": 27
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [],
      "metadata": {
        "id": "KXzoDvUiiTT9"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "results[\"RFE (3 best)\"] = rfe_f1_score_list[2] # correct item from rfe_f1_score_list\n",
        "pprint(results)\n",
        "\n",
        "fig, ax = plt.subplots()\n",
        "\n",
        "x = results.keys()\n",
        "y = results.values()\n",
        "\n",
        "ax.bar(x, y, width = 0.4, color='purple')\n",
        "\n",
        "#ax.set_xlabel('Features')\n",
        "ax.set_ylabel('F1-Score weighted')\n",
        "ax.set_ylim(0.8, 1.05)\n",
        "\n",
        "for idx, value in enumerate(y):\n",
        "  plt.text(x = idx, y = value + 0.01, s = str(round(value, 3)), ha = 'center')\n",
        "plt.tight_layout()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 736
        },
        "id": "h2vK0gYai8s3",
        "outputId": "41b096a8-b4c1-4c6a-f48a-3dfc97b8f0fb"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "{'K-best features (3 best)': 0.981,\n",
            " 'RFE (3 best)': 1.0,\n",
            " 'all features': 0.908,\n",
            " 'set variance threshold to 0.3': 0.963,\n",
            " 'set variance threshold to 0.31': 0.963}\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x800 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "RFE 3 best provides the perfect prediction"
      ],
      "metadata": {
        "id": "VCfZDy6i4M_i"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "4 Boruta (automated method)"
      ],
      "metadata": {
        "id": "oDA6mnaqjjx6"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "X_train_v5, X_test_v5, y_train_v5, y_test_v5 = X_train.copy(), X_test.copy(), y_train.copy(), y_test.copy()"
      ],
      "metadata": {
        "id": "KHTAZdosjREb"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "pip install boruta"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "KgwYYwUBlwQy",
        "outputId": "f98f65b6-4ff9-424f-c27a-36ebdd0a979e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Requirement already satisfied: boruta in /usr/local/lib/python3.10/dist-packages (0.3)\n",
            "Requirement already satisfied: numpy>=1.10.4 in /usr/local/lib/python3.10/dist-packages (from boruta) (1.26.4)\n",
            "Requirement already satisfied: scikit-learn>=0.17.1 in /usr/local/lib/python3.10/dist-packages (from boruta) (1.2.2)\n",
            "Requirement already satisfied: scipy>=0.17.0 in /usr/local/lib/python3.10/dist-packages (from boruta) (1.11.4)\n",
            "Requirement already satisfied: joblib>=1.1.1 in /usr/local/lib/python3.10/dist-packages (from scikit-learn>=0.17.1->boruta) (1.3.2)\n",
            "Requirement already satisfied: threadpoolctl>=2.0.0 in /usr/local/lib/python3.10/dist-packages (from scikit-learn>=0.17.1->boruta) (3.4.0)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "pip install --upgrade numpy Boruta"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "NyZlWQ_Jm91N",
        "outputId": "55dd7885-88ae-4b0e-ad1e-946e78faae43"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Requirement already satisfied: numpy in /usr/local/lib/python3.10/dist-packages (1.26.4)\n",
            "Requirement already satisfied: Boruta in /usr/local/lib/python3.10/dist-packages (0.3)\n",
            "Requirement already satisfied: scikit-learn>=0.17.1 in /usr/local/lib/python3.10/dist-packages (from Boruta) (1.2.2)\n",
            "Requirement already satisfied: scipy>=0.17.0 in /usr/local/lib/python3.10/dist-packages (from Boruta) (1.11.4)\n",
            "Requirement already satisfied: joblib>=1.1.1 in /usr/local/lib/python3.10/dist-packages (from scikit-learn>=0.17.1->Boruta) (1.3.2)\n",
            "Requirement already satisfied: threadpoolctl>=2.0.0 in /usr/local/lib/python3.10/dist-packages (from scikit-learn>=0.17.1->Boruta) (3.4.0)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from boruta import BorutaPy\n",
        "\n",
        "np.int = np.int32\n",
        "np.float = np.float64\n",
        "np.bool = np.bool_\n",
        "\n",
        "#boruta_selector = BorutaPy(gbc, n_estimators='auto', verbose=2, random_state = 42)\n",
        "boruta_selector = BorutaPy(gbc, random_state = 42)\n",
        "boruta_selector.fit(X_train_v5.values, y_train_v5.values.ravel())\n",
        "\n",
        "selected_X_train_v5 = boruta_selector.transform(X_train_v5.values)\n",
        "selected_X_test_v5 = boruta_selector.transform(X_test_v5.values) #.values\n",
        "\n",
        "gbc.fit(selected_X_train_v5, y_train_v5)\n",
        "boruta_predictions = gbc.predict(selected_X_test_v5)\n",
        "\n",
        "f1_score_boruta = round(f1_score(y_test_v5, boruta_predictions, average = 'weighted'), 3)"
      ],
      "metadata": {
        "id": "uzkxH5Iwkb1N"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "selected_features_mask = boruta_selector.support_\n",
        "\n",
        "selected_features = X_train_v4.columns[selected_features_mask]\n",
        "selected_features"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0H6BealClU67",
        "outputId": "1e453edb-aa96-4ee5-ad6f-32258d4d4029"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['malic_acid', 'ash', 'magnesium', 'flavanoids', 'proanthocyanins',\n",
              "       'color_intensity', 'hue', 'od280/od315_of_diluted_wines', 'proline'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {},
          "execution_count": 40
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "results[\"Boruta (9)\"] = f1_score_boruta\n",
        "pprint(results)\n",
        "\n",
        "fig, ax = plt.subplots()\n",
        "\n",
        "x = results.keys()\n",
        "y = results.values()\n",
        "\n",
        "ax.bar(x, y, width = 0.4, color='purple')\n",
        "\n",
        "ax.set_xlabel('Feature selection method')\n",
        "ax.set_ylabel('F1-Score weighted')\n",
        "ax.set_ylim(0.8, 1.05)\n",
        "\n",
        "for idx, value in enumerate(y):\n",
        "  plt.text(x = idx, y = value + 0.01, s = str(round(value, 3)), ha = 'center')\n",
        "fig.autofmt_xdate()\n",
        "plt.tight_layout()"
      ],
      "metadata": {
        "id": "kMb4Zhecu7Tu",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 754
        },
        "outputId": "77f8c971-2109-48cb-ac47-0f6538f0f11e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "{'Boruta (9)': 0.982,\n",
            " 'K-best features (3 best)': 0.981,\n",
            " 'RFE (3 best)': 1.0,\n",
            " 'all features': 0.908,\n",
            " 'set variance threshold to 0.3': 0.963,\n",
            " 'set variance threshold to 0.31': 0.963}\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x800 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "erQBHI_I6XK9"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}