Lab1_Linear_and_Ensembles.ipynb

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      "name": "Lab1_Linear_and_Ensembles.ipynb",
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      "source": [
        "# Лабораторная работа №1. \n",
        "\n",
        "Данная лабораторная работа состоит из нескольких блоков. В каждом блоке вам предлагается произвести некоторые манипуляции с данными и сделать некоторые выводы.\n",
        "* Задавать вопросы можно и нужно.\n",
        "* Списывать не нужно. Работы, которые были списаны обнуляются.\n",
        "* Блоки выполняются последовательно и оцениваются отдельно."
      ]
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    {
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      "source": [
        "!ls"
      ],
      "execution_count": 0,
      "outputs": [
        {
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          "text": [
            "car_data.csv  sample_data\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
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        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 54
        }
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      "source": [
        "\"\"\"\n",
        "from google.colab import files\n",
        "\n",
        "uploaded = files.upload()\n",
        "\n",
        "for fn in uploaded.keys():\n",
        "  print('User uploaded file \"{name}\" with length {length} bytes'.format(\n",
        "      name=fn, length=len(uploaded[fn])))\n",
        "#\"\"\""
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'\\nfrom google.colab import files\\n\\nuploaded = files.upload()\\n\\nfor fn in uploaded.keys():\\n  print(\\'User uploaded file \"{name}\" with length {length} bytes\\'.format(\\n      name=fn, length=len(uploaded[fn])))\\n#'"
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        "id": "8wS2jfPw6OhO",
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      },
      "source": [
        "## Часть 1. Работа с моделями и ансамблями моделей в задачи классификации."
      ]
    },
    {
      "cell_type": "markdown",
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      "source": [
        "### 1. Чтение данных.\n",
        "Воспользуемся [датасетом](https://archive.ics.uci.edu/ml/datasets/Statlog+%28Vehicle+Silhouettes%29), в котором описываются различные автомобили. Будем решать задачу многоклассовой ($k=4$) классификации.\n",
        "Для удобства, датасет уже преобразован в удобный формат."
      ]
    },
    {
      "cell_type": "code",
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        "colab": {
          "base_uri": "https://localhost:8080/",
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      },
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "from sklearn.model_selection import train_test_split\n",
        "import warnings\n",
        "warnings.filterwarnings('ignore')\n",
        "\n",
        "dataset = pd.read_csv('car_data.csv', delimiter=',', header=None).values\n",
        "data = dataset[:, :-1].astype(int)\n",
        "target = dataset[:, -1]\n",
        "#target = pd.get_dummies(target)\n",
        "print(data.shape, target.shape)\n",
        "\n",
        "X_train, X_test, y_train, y_test = train_test_split(data, target, test_size=0.35)\n",
        "print(X_train.shape, y_train.shape, X_test.shape, y_test.shape)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(846, 19) (846,)\n",
            "(549, 19) (549,) (297, 19) (297,)\n"
          ],
          "name": "stdout"
        }
      ]
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    {
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        "id": "5tiPwu__6OhX",
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      "source": [
        "Для первичного анализа может быть полезна библиотека `pandas`. Преобразуем `train` выборку в `pd.DataFrame`."
      ]
    },
    {
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      "source": [
        "X_train_pd = pd.DataFrame(X_train)\n",
        "\n",
        "# First 15 rows of our dataset.\n",
        "X_train_pd.head(15)"
      ],
      "execution_count": 0,
      "outputs": [
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              "3   599   93  39   63  146  58   7  128  ...  149  246  158  63   9   7  198  204\n",
              "4   770   80  44   68  120  53   8  151  ...  170  333  190  80   4  16  180  185\n",
              "5    46   91  43   70  133  55   8  130  ...  159  253  156  70   1   8  190  194\n",
              "6   346  117  52  110  228  65  12  212  ...  228  668  220  66  21  25  194  205\n",
              "7   418   87  43   70  169  72   7  152  ...  177  341  171  76   6  12  184  187\n",
              "8   111   94  48   83  162  64  10  156  ...  177  357  187  74   4  14  185  196\n",
              "9    59   85  39   68  119  52   5  128  ...  148  241  142  75   8   8  182  187\n",
              "10  128   97  45   88  173  67  10  157  ...  173  365  157  67   8  12  192  200\n",
              "11  441   90  47   73  136  58  11  161  ...  172  376  185  77  11   8  183  191\n",
              "12  663   89  47   83  169  61   8  164  ...  189  402  190  72   7  10  187  193\n",
              "13  591   80  36   69  127  56   7  128  ...  147  240  133  70   2  21  183  191\n",
              "14  313   96  52  104  222  67   9  198  ...  217  589  226  67  12  20  192  201\n",
              "\n",
              "[15 rows x 19 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "nbgrader": {
          "grade": false,
          "grade_id": "cell-98e7d91d77d65fcf",
          "locked": true,
          "schema_version": 2,
          "solution": false
        },
        "id": "fNsiXnmX6Ohc",
        "colab_type": "text"
      },
      "source": [
        "Воспользовавшись методами `describe` и `info` можно получить полезную информацию о датасете."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GIBTKhWS6Ohd",
        "colab_type": "code",
        "outputId": "b382a6da-7014-4d5f-cfec-b32510ede246",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 317
        }
      },
      "source": [
        "X_train_pd.describe()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>0</th>\n",
              "      <th>1</th>\n",
              "      <th>2</th>\n",
              "      <th>3</th>\n",
              "      <th>4</th>\n",
              "      <th>5</th>\n",
              "      <th>6</th>\n",
              "      <th>7</th>\n",
              "      <th>8</th>\n",
              "      <th>9</th>\n",
              "      <th>10</th>\n",
              "      <th>11</th>\n",
              "      <th>12</th>\n",
              "      <th>13</th>\n",
              "      <th>14</th>\n",
              "      <th>15</th>\n",
              "      <th>16</th>\n",
              "      <th>17</th>\n",
              "      <th>18</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>549.000000</td>\n",
              "      <td>549.000000</td>\n",
              "      <td>549.000000</td>\n",
              "      <td>549.000000</td>\n",
              "      <td>549.000000</td>\n",
              "      <td>549.000000</td>\n",
              "      <td>549.000000</td>\n",
              "      <td>549.000000</td>\n",
              "      <td>549.000000</td>\n",
              "      <td>549.000000</td>\n",
              "      <td>549.000000</td>\n",
              "      <td>549.000000</td>\n",
              "      <td>549.000000</td>\n",
              "      <td>549.000000</td>\n",
              "      <td>549.000000</td>\n",
              "      <td>549.000000</td>\n",
              "      <td>549.000000</td>\n",
              "      <td>549.000000</td>\n",
              "      <td>549.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>425.588342</td>\n",
              "      <td>93.360656</td>\n",
              "      <td>44.637523</td>\n",
              "      <td>81.728597</td>\n",
              "      <td>167.510018</td>\n",
              "      <td>61.482696</td>\n",
              "      <td>8.506375</td>\n",
              "      <td>167.806922</td>\n",
              "      <td>41.234973</td>\n",
              "      <td>20.495446</td>\n",
              "      <td>147.433515</td>\n",
              "      <td>187.642987</td>\n",
              "      <td>434.602914</td>\n",
              "      <td>173.785064</td>\n",
              "      <td>72.606557</td>\n",
              "      <td>6.315118</td>\n",
              "      <td>12.766849</td>\n",
              "      <td>188.779599</td>\n",
              "      <td>195.422587</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>246.179786</td>\n",
              "      <td>8.396275</td>\n",
              "      <td>6.220825</td>\n",
              "      <td>15.870631</td>\n",
              "      <td>33.289572</td>\n",
              "      <td>7.663913</td>\n",
              "      <td>4.640984</td>\n",
              "      <td>33.281687</td>\n",
              "      <td>7.881269</td>\n",
              "      <td>2.579127</td>\n",
              "      <td>14.703817</td>\n",
              "      <td>31.680834</td>\n",
              "      <td>176.521010</td>\n",
              "      <td>33.154159</td>\n",
              "      <td>7.861369</td>\n",
              "      <td>4.916174</td>\n",
              "      <td>8.932651</td>\n",
              "      <td>6.174224</td>\n",
              "      <td>7.608097</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>77.000000</td>\n",
              "      <td>33.000000</td>\n",
              "      <td>40.000000</td>\n",
              "      <td>104.000000</td>\n",
              "      <td>47.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>112.000000</td>\n",
              "      <td>26.000000</td>\n",
              "      <td>17.000000</td>\n",
              "      <td>118.000000</td>\n",
              "      <td>130.000000</td>\n",
              "      <td>184.000000</td>\n",
              "      <td>112.000000</td>\n",
              "      <td>59.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>176.000000</td>\n",
              "      <td>181.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>217.000000</td>\n",
              "      <td>87.000000</td>\n",
              "      <td>39.000000</td>\n",
              "      <td>70.000000</td>\n",
              "      <td>140.000000</td>\n",
              "      <td>57.000000</td>\n",
              "      <td>7.000000</td>\n",
              "      <td>146.000000</td>\n",
              "      <td>33.000000</td>\n",
              "      <td>19.000000</td>\n",
              "      <td>136.000000</td>\n",
              "      <td>167.000000</td>\n",
              "      <td>317.000000</td>\n",
              "      <td>147.000000</td>\n",
              "      <td>67.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>6.000000</td>\n",
              "      <td>184.000000</td>\n",
              "      <td>189.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>424.000000</td>\n",
              "      <td>92.000000</td>\n",
              "      <td>44.000000</td>\n",
              "      <td>79.000000</td>\n",
              "      <td>166.000000</td>\n",
              "      <td>61.000000</td>\n",
              "      <td>8.000000</td>\n",
              "      <td>156.000000</td>\n",
              "      <td>43.000000</td>\n",
              "      <td>19.000000</td>\n",
              "      <td>145.000000</td>\n",
              "      <td>177.000000</td>\n",
              "      <td>357.000000</td>\n",
              "      <td>173.000000</td>\n",
              "      <td>72.000000</td>\n",
              "      <td>5.000000</td>\n",
              "      <td>11.000000</td>\n",
              "      <td>188.000000</td>\n",
              "      <td>196.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>637.000000</td>\n",
              "      <td>99.000000</td>\n",
              "      <td>49.000000</td>\n",
              "      <td>98.000000</td>\n",
              "      <td>195.000000</td>\n",
              "      <td>65.000000</td>\n",
              "      <td>10.000000</td>\n",
              "      <td>198.000000</td>\n",
              "      <td>46.000000</td>\n",
              "      <td>23.000000</td>\n",
              "      <td>159.000000</td>\n",
              "      <td>216.000000</td>\n",
              "      <td>586.000000</td>\n",
              "      <td>195.000000</td>\n",
              "      <td>76.000000</td>\n",
              "      <td>9.000000</td>\n",
              "      <td>19.000000</td>\n",
              "      <td>193.000000</td>\n",
              "      <td>201.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>844.000000</td>\n",
              "      <td>119.000000</td>\n",
              "      <td>59.000000</td>\n",
              "      <td>110.000000</td>\n",
              "      <td>333.000000</td>\n",
              "      <td>138.000000</td>\n",
              "      <td>55.000000</td>\n",
              "      <td>261.000000</td>\n",
              "      <td>61.000000</td>\n",
              "      <td>28.000000</td>\n",
              "      <td>188.000000</td>\n",
              "      <td>320.000000</td>\n",
              "      <td>998.000000</td>\n",
              "      <td>268.000000</td>\n",
              "      <td>135.000000</td>\n",
              "      <td>22.000000</td>\n",
              "      <td>41.000000</td>\n",
              "      <td>204.000000</td>\n",
              "      <td>211.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "               0           1           2   ...          16          17          18\n",
              "count  549.000000  549.000000  549.000000  ...  549.000000  549.000000  549.000000\n",
              "mean   425.588342   93.360656   44.637523  ...   12.766849  188.779599  195.422587\n",
              "std    246.179786    8.396275    6.220825  ...    8.932651    6.174224    7.608097\n",
              "min      0.000000   77.000000   33.000000  ...    0.000000  176.000000  181.000000\n",
              "25%    217.000000   87.000000   39.000000  ...    6.000000  184.000000  189.000000\n",
              "50%    424.000000   92.000000   44.000000  ...   11.000000  188.000000  196.000000\n",
              "75%    637.000000   99.000000   49.000000  ...   19.000000  193.000000  201.000000\n",
              "max    844.000000  119.000000   59.000000  ...   41.000000  204.000000  211.000000\n",
              "\n",
              "[8 rows x 19 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kAiQNjat6Ohh",
        "colab_type": "code",
        "outputId": "5fd8f344-fad6-4f84-8248-de2d6b21001e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 425
        }
      },
      "source": [
        "X_train_pd.info()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 549 entries, 0 to 548\n",
            "Data columns (total 19 columns):\n",
            "0     549 non-null int64\n",
            "1     549 non-null int64\n",
            "2     549 non-null int64\n",
            "3     549 non-null int64\n",
            "4     549 non-null int64\n",
            "5     549 non-null int64\n",
            "6     549 non-null int64\n",
            "7     549 non-null int64\n",
            "8     549 non-null int64\n",
            "9     549 non-null int64\n",
            "10    549 non-null int64\n",
            "11    549 non-null int64\n",
            "12    549 non-null int64\n",
            "13    549 non-null int64\n",
            "14    549 non-null int64\n",
            "15    549 non-null int64\n",
            "16    549 non-null int64\n",
            "17    549 non-null int64\n",
            "18    549 non-null int64\n",
            "dtypes: int64(19)\n",
            "memory usage: 81.6 KB\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "nbgrader": {
          "grade": false,
          "grade_id": "cell-be844269be69c387",
          "locked": true,
          "schema_version": 2,
          "solution": false
        },
        "id": "DTE294jb6Ohk",
        "colab_type": "text"
      },
      "source": [
        "### 2. Работа с данными, построение модели, анализ ошибки.\n",
        "Выполните следующие манипуляции с данными:"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "BfhaXkSL6Ohl",
        "colab_type": "text"
      },
      "source": [
        "#### 2.0. Предобработка данных.\n",
        "* Произведите необходимые (по вашему мнению) манипуляции с данными и объясните их."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "nbgrader": {
          "grade": true,
          "grade_id": "cell-a1514aa189a49fca",
          "locked": false,
          "points": 15,
          "schema_version": 2,
          "solution": true
        },
        "id": "etr1vLDO6Ohm",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from sklearn import preprocessing\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xdJbJH__npN0",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#Масштабируем фичи во избежении превосходства одних над другими и ускорения обучения\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "normalizer = preprocessing.MinMaxScaler(feature_range=(-1,1))\n",
        "X_train_norm_np = normalizer.fit_transform(X_train)\n",
        "X_train_norm_pd = pd.DataFrame(data=X_train_norm_np)\n",
        "X_test_norm_np = normalizer.transform(X_test)\n",
        "X_test_norm_pd = pd.DataFrame(data=X_test_norm_np)\n",
        "#Масштабируем фичи во избежении превосходства одних над другими и ускорения обучения"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "e_aKEuwloBpp",
        "colab_type": "code",
        "outputId": "f41cf1ce-c551-46d9-bc7b-2fdf4b505baf",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 503
        }
      },
      "source": [
        "#Проверим фичи на коррелированность\n",
        "corr = X_train_norm_pd.corr()\n",
        "plt.figure(figsize=(10, 8))\n",
        "sns.heatmap(corr, vmax=1, vmin=-1, square=True, cmap='magma')"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fe5e744be10>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 9
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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4MU3zTP2STpS0YcR+S7o2ZUQAAKBRuhUjF0uaExE3jjxge1nKiAAAaLhW1QOY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\nVIpiBAAAVOr/A/lZUYscZWiqAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 720x576 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BfhDuBm1onEt",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "high_corr_idx = [7, 9] #Уровень корреляции фичей 7, 9, 12 больше 0.99 по модулю\n",
        "#Убирём эти фиши для уменьшеня шума и увеличения устойчивости\n",
        "X_train_prep_pd = X_train_norm_pd.drop(high_corr_idx, 1)\n",
        "X_test_prep_pd = X_test_norm_pd.drop(high_corr_idx, 1)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "UYLeLqJJ6Ohp",
        "colab_type": "text"
      },
      "source": [
        "#### 2.1. Базовая логистическая регрессия.\n",
        "* Подберите оптимальные параметры логистической регресии с помощью кросс-валидации на train-датасете (е переусердствуйте с подбором, в данной работе не стоит задача найти самую оптимальную модель. Небольшого grid/random search'а хватит).\n",
        "\n",
        "* Постройте график ROC-кривой для данного классификатора (`sklearn.metrics.roc_curve`), оцените точность классификации и f1-score. "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "nbgrader": {
          "grade": true,
          "grade_id": "cell-1dd5ad5d0845cbbb",
          "locked": false,
          "points": 5,
          "schema_version": 2,
          "solution": true
        },
        "id": "sN99CBwp6Ohq",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from sklearn.linear_model import LogisticRegression\n",
        "from sklearn.metrics import accuracy_score, roc_curve, f1_score, auc\n",
        "from sklearn.preprocessing import MultiLabelBinarizer\n",
        "from sklearn.model_selection import GridSearchCV\n",
        "from sklearn.pipeline import Pipeline"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XDc55tILqyIz",
        "colab_type": "code",
        "outputId": "e216daad-901a-47b2-df57-2cd61f30b9d7",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 238
        }
      },
      "source": [
        "tuned_parameters = { 'C' : [0.1, 1, 10, 100, 1000, 10000]}\n",
        "clf = GridSearchCV(LogisticRegression(), tuned_parameters, cv=5)\n",
        "clf.fit(X_train_prep_pd, y_train)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "GridSearchCV(cv=5, error_score='raise-deprecating',\n",
              "             estimator=LogisticRegression(C=1.0, class_weight=None, dual=False,\n",
              "                                          fit_intercept=True,\n",
              "                                          intercept_scaling=1, l1_ratio=None,\n",
              "                                          max_iter=100, multi_class='warn',\n",
              "                                          n_jobs=None, penalty='l2',\n",
              "                                          random_state=None, solver='warn',\n",
              "                                          tol=0.0001, verbose=0,\n",
              "                                          warm_start=False),\n",
              "             iid='warn', n_jobs=None,\n",
              "             param_grid={'C': [0.1, 1, 10, 100, 1000, 10000]},\n",
              "             pre_dispatch='2*n_jobs', refit=True, return_train_score=False,\n",
              "             scoring=None, verbose=0)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FC9U4VaFEmlE",
        "colab_type": "code",
        "outputId": "664a9223-6b9c-47ee-c0e7-c451377d9937",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "clf.best_params_"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'C': 1000}"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "crCV4Z3ffA54",
        "colab_type": "code",
        "outputId": "691c75a4-79c3-4017-eee2-2e4dd3f4bdb0",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        }
      },
      "source": [
        "print('score:', clf.score(X_test_prep_pd, y_test))\n",
        "print('weighted f1 score:', f1_score(y_test, clf.predict(X_test_prep_pd), average='weighted'))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "score: 0.7710437710437711\n",
            "weighted f1 score: 0.7702820126835858\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "exyF_3esfMbJ",
        "colab_type": "code",
        "outputId": "02cf5348-02fa-4ef7-a7f4-14f7e1a892b8",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "target_types = np.unique(target)\n",
        "print(target_types)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "['bus' 'opel' 'saab' 'van']\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "IBQdrD9lZY4h",
        "colab_type": "code",
        "outputId": "d7148732-7f78-4187-c45d-b0c6c22e33e3",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        }
      },
      "source": [
        "def show_roc(clf, X_test, y_test):\n",
        "  target_types = np.unique(y_test)\n",
        "  for i in range(4):\n",
        "    fpr, tpr, thresholds = roc_curve(y_test, clf.predict_proba(X_test)[:, i], pos_label=target_types[i])\n",
        "    area = auc(fpr, tpr)\n",
        "    plt.plot(fpr, tpr, label='%s (AUC = %0.2f)' % (target_types[i], area))\n",
        "  plt.xlim([-0.05, 1.05])\n",
        "  plt.ylim([-0.05, 1.05])\n",
        "  plt.xlabel('False Positive Rate')\n",
        "  plt.ylabel('True Positive Rate')\n",
        "  plt.title('Receiver operating characteristic')\n",
        "  plt.legend(loc=\"lower right\")\n",
        "  plt.show()\n",
        "\n",
        "show_roc(clf, X_test_prep_pd, y_test)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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MpLCYvn46I+aNYMS8Ed5WAnDmTiNfdteRMQXm3XffZcCAAURERHiXJSQk0LFj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\nqxSMMcZ4WaVgjDHGyyoFY4wxXv8PBn61X94yHLkAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "K5TcCN9u6Ohu",
        "colab_type": "text"
      },
      "source": [
        "#### 2.2. Зависимость объясненной дисперсии от числа главных компонент.\n",
        "Воспользуемся методом главных компонент (PCA). \n",
        "\n",
        "Примените его к train-части данных.\n",
        "Постройте график зависимости объясненной дисперсии (explained variance ratio) от количества главных компонент."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "nbgrader": {
          "grade": true,
          "grade_id": "cell-c6c614740bce090e",
          "locked": false,
          "points": 10,
          "schema_version": 2,
          "solution": true
        },
        "id": "vcR0jKUo6Ohv",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from sklearn.decomposition import  PCA"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vQzLqFXbKiBr",
        "colab_type": "code",
        "outputId": "24252dc0-4669-44d7-af70-f53af9d1603e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 334
        }
      },
      "source": [
        "# Отметим, что в данном случае PCA применяется к масштабированным фичам во избежании большей дисперсии по определённым направлениям.\n",
        "# Также отметим, что если применить PCA к немасштабированным данным, то в данном случае точность классификаци упадёт более чем в два раза.\n",
        "pca = PCA()\n",
        "pca.fit(X_train_prep_pd)\n",
        "variance = pca.explained_variance_ratio_\n",
        "features_num = len(variance)\n",
        "for i in range(features_num - 1):\n",
        "  variance[i+1] += variance[i]\n",
        "plt.xlabel('number of principal components')\n",
        "plt.ylabel('explained variance ratio')\n",
        "plt.plot(np.arange(features_num) + 1, variance)\n",
        "plt.show()\n",
        "print(variance)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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EOafQl6Al0UqCQ0AjzMzcvc1j++4+jWBgnuRlNydNO8FY0De0J3RHmV5azsSh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\niIhIgoqCiIgk/H/Kn4hp3iCLiwAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "[0.46295009 0.64757874 0.76732657 0.84295041 0.90476621 0.93212325\n",
            " 0.95718541 0.97029636 0.98173468 0.98709234 0.99152549 0.99478056\n",
            " 0.99666185 0.99806669 0.99897329 0.99964591 1.        ]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "nbgrader": {
          "grade": false,
          "grade_id": "cell-0c1fe666f52fe53c",
          "locked": true,
          "schema_version": 2,
          "solution": false
        },
        "id": "xVlcgLdn6Oh0",
        "colab_type": "text"
      },
      "source": [
        "#### 2.3. Преобразование с помощью PCA.\n",
        "Выберите определенное число компонент. Кратко опишите, чем обусловлен ваш выбор.\n",
        "\n",
        "Используя эти главные компоненты, преобразуйте train и test выборки (используя методы `fit` и `transform`)."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "nbgrader": {
          "grade": true,
          "grade_id": "cell-96ab18d96473ef71",
          "locked": false,
          "points": 5,
          "schema_version": 2,
          "solution": true
        },
        "id": "2GcAI5JT6Oh1",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Оставим первые 11 компонент, тк они описывают большую (>99%) часть дисперсии.\n",
        "n_pca = 11\n",
        "pca = PCA(n_components=n_pca)\n",
        "pca.fit(X_train_prep_pd)\n",
        "X_test_pca = pca.transform(X_test_prep_pd)\n",
        "X_train_pca = pca.transform(X_train_prep_pd)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "nbgrader": {
          "grade": false,
          "grade_id": "cell-d28b58a35c94e988",
          "locked": true,
          "schema_version": 2,
          "solution": false
        },
        "id": "Kfn-lTvg6Oh6",
        "colab_type": "text"
      },
      "source": [
        "#### 2.4. Логистическая регрессия над преобразованными данными.\n",
        "* Подберите оптимальные параметры логистической регресии с помощью кросс-валидации на преобразованном train-датасете.\n",
        "\n",
        "* Постройте график ROC-кривой для полученных классификаторов, оцените точность классификации и f1-score. "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "outputId": "83fec137-08ab-4701-df26-8273fbc12648",
        "id": "K8IRWLGdtAXr",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 85
        }
      },
      "source": [
        "tuned_parameters = { 'C' : [0.1, 1, 10, 100, 1000, 10000]}\n",
        "clf_pca = GridSearchCV(LogisticRegression(), tuned_parameters, cv=5)\n",
        "clf_pca.fit(X_train_pca, y_train)\n",
        "print('Лучший параметр регуляризации:', clf_pca.best_params_)\n",
        "print('Точность классификации упала примерно на 7%')\n",
        "print('score:', clf_pca.score(X_test_pca, y_test))\n",
        "print('weighted f1 score:', f1_score(y_test, clf_pca.predict(X_test_pca), average='weighted'))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Лучший параметр регуляризации: {'C': 10}\n",
            "Точность классификации упала примерно на 7%\n",
            "score: 0.7070707070707071\n",
            "weighted f1 score: 0.6954865583853265\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "outputId": "59ee3442-e22a-4074-947f-d22affffc9e2",
        "id": "GC3_rH2XtAYJ",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 312
        }
      },
      "source": [
        "show_roc(clf_pca, X_test_pca, y_test)\n",
        "print('ROC кривая тоже уменьшила свою площадь')"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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rm10Z7jAih2+zkTUJmRhnhYIxgbBmIxMnrFCIQ8UNOY36oaVlzTnkj40kMnHECoVi+HYu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\nZYWCMcYYLysUjDHGeFmhYIwxxuv/AdLXHIchd2TiAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "ROC кривая тоже уменьшила свою площадь\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "nbgrader": {
          "grade": false,
          "grade_id": "cell-4fbf16c64076e139",
          "locked": true,
          "schema_version": 2,
          "solution": false
        },
        "id": "9cRNpFbR6Oh-",
        "colab_type": "text"
      },
      "source": [
        "#### 2.5. Решающее дерево.\n",
        "Рассмотрим поведение решающего дерева на исходных и преобразованных данных. Будем варьировать лишь один параметр - максимальную глубину дерева. \n",
        "\n",
        "* С помощью кросс-валидации подберите оптимальный параметр `max_depth` и оцените на исходных и преобразованных данных те же метрики, что и выше."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "nbgrader": {
          "grade": true,
          "grade_id": "cell-748ed20b51c67fab",
          "locked": false,
          "points": 15,
          "schema_version": 2,
          "solution": true
        },
        "id": "5qzpyx_U6OiA",
        "colab_type": "code",
        "outputId": "0bf89a0c-f0a2-40d8-dbe6-4c9b820f989e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "from sklearn.tree import DecisionTreeClassifier\n",
        "max_depth=2\n",
        "tuned_parameters = { 'max_depth' : [1, 2, 3, 4, 5, 6, 7, 9, 12, 14, 17]}\n",
        "clf_tree = GridSearchCV(DecisionTreeClassifier(), tuned_parameters, cv=5)\n",
        "print('Предобработанные данные')\n",
        "clf_tree.fit(X_train_prep_pd, y_train)\n",
        "print('Лучший параметр:', clf_tree.best_params_)\n",
        "print('score:', clf_tree.score(X_test_prep_pd, y_test))\n",
        "print('weighted f1 score:', f1_score(y_test, clf_tree.predict(X_test_prep_pd), average='weighted'))\n",
        "show_roc(clf_tree, X_test_prep_pd, y_test)\n",
        "print('Исходные данные')\n",
        "clf_tree.fit(X_train, y_train)\n",
        "print('Лучший параметр:', clf_tree.best_params_)\n",
        "print('score:', clf_tree.score(X_test, y_test))\n",
        "print('weighted f1 score:', f1_score(y_test, clf_tree.predict(X_test), average='weighted'))\n",
        "show_roc(clf_tree, X_test, y_test)\n",
        "print('PCA данные')\n",
        "clf_tree.fit(X_train_pca, y_train)\n",
        "print('Лучший параметр:', clf_tree.best_params_)\n",
        "print('score:', clf_tree.score(X_test_pca, y_test))\n",
        "print('weighted f1 score:', f1_score(y_test, clf_tree.predict(X_test_pca), average='weighted'))\n",
        "show_roc(clf_tree, X_test_pca, y_test)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Предобработанные данные\n",
            "Лучший параметр: {'max_depth': 5}\n",
            "score: 0.622895622895623\n",
            "weighted f1 score: 0.6323377617318413\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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PuW2/CoPavbnuTZRS3DRIlxI0rb4ErBVVKeUGbge+ATZh9DLaICKPisj55mb3\nADeJyBpgDjBeKRWQ6qGauHcYPY8ievcKxuG18vaugsM7IOkGaNe/QkLYn7ef+dvmc0HiBbqUoGn1\nKKBtCuY9B4vKLZvq83wjcFIgY/Bbyk7ybaF0Segc7Eg0gHUfgzUU+p9f6epZ62cZpQTdlqBp9Ur3\ntzTZ01JIjWpLTLgeLyfovB5YPx96nwmOmAqrD+Qd4OOtH3NB4gV0iuxUyQ40TasrnRRMYftS2RPT\nXg9h0Rjs/BVyD8Cgyypd/eb6N3UpQdMCRCcFwJOVRVh2Jumtdd10o7BuHoRGQ6+zKqw6kHeA+Vvn\n61KCpgWITgpA0fYdABxpq9sTgs5VABsXQr/zwe6osHrW+ll4lZcbB90YhOA0rfnTSQEo2m4MhJfX\nvksNW2oBt+1bcObA4IpVRwfzD/Lx1o85P/F8OkfpBK5pgaCTAuBM3o7TZsfTVk+nGXRrP4LIdtCt\n4jSaupSgaYGnkwLg3LWLA9HtiAkPDXYoLVvBEaOkMPASsJRt8D+Yf5B5W+ZxfuL5dInSJTpNCxSd\nFAB3ejoZjmiiw3R31KDa9Dl4nJX2Opq1fhYe5dGlBE0LMJ0UAHdGBun2SKIdOikE1bqPoHVP6Di0\nzOL0/HSjLaGnLiVoWqD5lRREJEREEgMdTDAorxf3oUMcDo0iRpcUgid7H+xcbJQSyg1pMWv9LNxe\nt74vQdMaQI1JQUTOBdYB35mvh4jIp4EOrKF4jhwBj4fDjiiiw4I9kngLtuETQFWoOkrPT2fe1nmM\n7TlWlxI0rQH4U1J4FGNynCMASqnVQLMpNbjTMwDIDI3S1UfBtPYjo9oovuyfVnEp4eZBNwcpME1r\nWfxJCi6l1JFyy4IykmkguDOMkbozHdG6+ihYMrbBvtXVlxKidSlB0xqCP/Ulm0Tk/wCLOTfCncDS\nwIbVcDwZRknhcGiU7n0ULOvmAWJ0RfWhSwma1vD8KSncDhwLeIFPgCJgYiCDakhuMylkOnT1UVAo\nZSSF7qdAVPuSxRkFGczbOo/zepynSwma1oD8SQpnKaWmKKWGmo/7gbMDHVhDcadn4Al1UGgL1dVH\nwVA8mU65qqOSUsJgXUrQtIbkT1J4sJJl/6rvQILFnZFBYVQsAJEO3fuowa2dV2EynYyCDD7a8hHn\n9jiXhOiEIAanaS1PlWdBETkLGAN0EpHnfFZFY1QlNQvujAzyI2OJCrVhtUjNb9DqTxWT6by1/i3c\nXje3DL4liMFpWstU3aXxQWA9UAhs8FmeA9wfyKAakjsjnZzwON3IHAw7f4W8g2WqjnQpQdOCq8qk\noJT6C/hLRN5XShU2YEwNypOYxvCYAAAgAElEQVSeQVbP7jopBEMlk+m8tf4tnF6nbkvQtCDxpxK9\nk4g8AfQHSmY9UUr1DlhUDcTrdOLJyjK6o+r2hIZVPJlO/wtKJtNJy05jzuY5jO0xlq7RXYMcoKa1\nTP40NM8G3gIEo9fRR8CHAYypwRTfo5AeEqlLCg2teDKdQZcC4FVe/rPiP9gsNu4cdmeQg9O0lsuf\ny+NwpdQ3IjJdKbUdeFBEVgAPBTi2gCu+R+GANVx3R21gnjUfsiW2PcsLUljx4x2sOrCKbGc2E4dN\npG1422CHp2ktlj9JoUhELMB2EfkHsAeICmxYDaM4Key1hJOkb1wLKLfXzZbDW1i+fzkr9v7BqsI1\n5LQKgZXP0iWqC6cnnM7xHY5nTLcxwQ5V01o0f5LC3UAExvAWTwAxwPWBDKqhFA+Gt9caoUdIDZDD\nhYd5YukTLNm7hDxXHgDdQlpxZm4+w0+8h6S+l9EuQk+DqmmNRY1nQqXUn+bTHODvACLSKZBBNZTi\nwfCOhEbq6qMA2JW1i1t/uJWD+Qc5v+f5DG8/nKR2SbSZdz2oXBh2W4W5EzRNC65qk4KIDAc6Ab8p\npTJEZAAwBfgb0LkB4gsod0YGxMTgttj0uEf1bNWBVdz5051YxcqbZ73JMW2OMVYUT6Zz6hSdEDSt\nEaqy95GIPAW8D1wFfC0ijwA/AWuAJt8dFYzeR6pVHIDufVSPMgszuf3H22kV2or3zn6vNCGAcQdz\nJZPpaJrWOFRXUrgAOEYpVSAirYE0YJBSakfDhBZ47vQMXDGtAHT1UT2asXoG+a583j373YojnK6b\nV+lkOpqmNQ7V3adQqJQqAFBKHQa2NqeEAEb1UVGUkRR0Q3P92HJ4C/O2zmNc33H0jO1ZdmX61kon\n09E0rfGo7kzYQ0Q+MZ8L0N3nNUqpi2vauYiMAV4ErMAbSqmnK9nm/4BHMGZzW6OUutL/8OtOKYU7\nI4OCQcZAbLpN4egppXhm+TNEh0Qz4ZgJZVe6CuCzCWALqzCZjqZpjUd1SaH8f+7LtdmxiFiBGcAZ\nwG5guYgsVEpt9NmmF/BP4CSlVKaINNhdS968PFRhIbnhMeDR1Uf14YfUH1i2fxn/Ov5fxISWjnqK\n1wuf3Ax7VsLl75WZTEfTtMalugHxfjjKfR8HJBdXOYnIXIx2io0+29wEzFBKZZrHPHiUx/SbO93o\njpoVFoU1XwgPsTbUoZulIk8R01dMJzE2kUt7X1p25Q+PwKaFcNaT0O+8oMSnaZp//Bn7qK46YTRO\nF9ttLvPVG+gtIktEZKlZ3VSBiNwsIitEZEW6eTI/WmXmZnbYEN098qi8u/Fd9uTuYcpxU7BZfK41\ntv8IS16EpBtgxK3BC1DTNL8EMin4wwb0AkYBVwCvi0hs+Y2UUjOVUklKqaQ2bdrUy4GLh7jICInS\nVUdHKSU7hZlrZ3J6wumM6DCidEVRLnw+EeJ6GaUEnXg1rdHzOymISGgt970H8O2P2Nlc5ms3sFAp\n5VJK7QS2YiSJgCse4uKALULfo3AU8l353P3z3YRaQ5kyfErZlT89AUdS4fz/lgyPrWla41ZjUhCR\n40RkHbDNfH2MiPzXj30vB3qJSHcRCQHGAQvLbfMZRikBEYnHqE5qkG6v7owMsNk4QIjueVRHSimm\n/TGN5Mxk/n3Kv+kQ2aF0ZdpyWPoqDL8Jup4QvCA1TasVf0oKLwHnAYcAlFJrgNNqepNSyg3cDnwD\nbAI+UkptEJFHRaR4lvZvgEMishHjbul7lVKHav8xas+dkYEtLo6sQo+uPqqjOZvnsGjnIu4Yegcn\ndjyxdIW7CBbeDtGdYPTDwQtQ07Ra8+eOLYtSKqVcQ6zHn50rpRYBi8otm+rzXAGTzEeDcmekY4uP\nJ7vQrW9cq4PVB1fzn+X/YVSXUdww6IayKxc/B+mb4cp5ENosRlnXtBbDn7NhmogcByjz3oM7MOr+\nmzR3Rgb2Nm3JKnDp6qNayijI4J6f76FjZEeeOPkJLOJT4DywARY/C4Mvh95nBi9ITdPqxJ/qowkY\nV/IJwAFghLmsSfOkZ0BcHE63Vzc014LL62LyL5PJdmbz/GnPEx0SXbrS64GFd4AjGs56KnhBappW\nZ/6UFNxKqXEBj6QBKY8H9+HDeGJbw2E9QmptvLjyRVYeWMlTI5+id6tyg+X++Zpx1/Ilb0JEXHAC\n1DTtqPhTUlguIotE5FoRaRYVxJ4jR8DjoSjKuCUi2qHbFPzxza5veHvj21zZ90rO61HuzuTDO+GH\nx6D3GD22kaY1YTUmBaVUT+Bx4FhgnYh8JiJNuuRQfONagZkUdO+jmu04soOHljzEMW2OYXLS5LIr\nlTJuUrPY4Nzn9E1qmtaE+XXzmlLqd6XUncAwIBtj8p0mq/jGtdwIc4RUnRSqlevMZeJPEwmzhfHs\nqc9it5b7vv56D3b+Amc+CjHNYqZWTWux/Ll5LVJErhKRz4FlQDpwYg1va9SK52bODtPDZtdEKcXU\n36eSlpPG9FOn0y6iXdkNcvbDN/+CrifBsPFBiVHTtPrjT2X6euBz4Bml1OIAx9MgigfDywyNBA7q\n6qNqvL3hbb5L+Y7JSZMZ3n54xQ0WTQZPEYx9CSzBHkpL07Sj5U9S6KGU8gY8kgbkTs/AEh7OEYxk\nEKUbmiu1bN8ynl/1PGd0PYNr+l9TcYONC2DT5zD6ET29pqY1E1WeDUXkWaXUPcB8EVHl1/sz81pj\n5U5Px9omnqwCF6E2Cw67nkuhvP15+7n313vpGt2Vx056rOLQ4vmH4cvJ0OEYOOGO4ASpaVq9q+4S\n+UPzZ61mXGsK3BkZ2OLbkF3g0lVHlXB5XNzzyz0Uugt566y3iLBHVNzo24cg/xBcPR+suqSlac1F\ndTOvLTOf9lNKlUkMInI7cLQzswWNOyOD0F69yC506Z5HlXhm+TOsTV/Ls6c+S4/YHhU32P4jrH4P\nTp4EHQY3fICapgWMPy2D11ey7IZKljUZRkkh3hz3SF/l+vp8++fM3TKX8QPGc2a3cmMXKQV/vQ8f\nXQtxiXDqlMp3omlak1Vdm8LlGHMgdBeRT3xWRQFHAh1YoHiLivBmZ2NrE092gZv4yJBgh9RobDm8\nhUf/eJTh7YczcdjEsiuz98EXd8HWryHhRLjoVT1xjqY1Q9VdJi/DmEOhMzDDZ3kO8Fcggwqk4u6o\ntvh4sne66NGmkvryFiirKIu7frqL6JBonjnlmdJ5lpWCdfNg0b3GPAljnobjbtHdTzWtmaquTWEn\nsBP4vuHCCbziIS6s8fFkbXTqG9cAr/Lyr9/+xf78/bx11lvEh8UbK3IPwhd3w+YvoPNxcOGruuup\npjVz1VUf/aKUOlVEMgHfLqmCMT9O64BHFwBu35JCQYqeYAd4fe3r/LL7Fx44/gGGtB1iLFw/3+hy\n6syDMx6DE24Di+66q2nNXXVnxOIpN+MbIpCGUjzuUVF0K7wqpcV3SV2yZwkzVs/gvB7nMa7POMjL\ngC/vgY2fQcdhcNFr0KZPsMPUNK2BVFd9VHwXcxdgr1LKKSInA4OB9zAGxmtyiksKueHGKOAtufpo\nT+4epiyeQq9WvZh6wlRk0+dGdVFhFpw+FU6cqO9B0LQWxp/Wws8wpuLsCbwF9AI+CGhUAeTOSMfa\nqhU5LuN1S71PochTxN0/3Y3X6+X5EQ8TtuAO+Ojvxiint/wCI+/RCUHTWiB//uu9SimXiFwM/Fcp\n9ZKINNneR8X3KGQWGFmhpVYfPfnnk2w6vIn/9hlPwjuXGncnj3oARk6C8kNja5rWYvg1HaeIXAb8\nHbjQXNZkzxqe9Axs5rhH0DKrj+Zvnc8n2z7h5pAujPr6UWg3EK76WN+drGma33c0n4YxdPYOEekO\nzAlsWIHjzsjAGh9PdqEboMX1PlqfsZ4nlj7GiU4vt25dCqfcBzf9pBOCpmmAHyUFpdR6EbkTSBSR\nvkCyUuqJwIdW/5RSZQbDg5ZVfZSZlcqkReNp4yzi384orDd+D52GBTssTdMakRqTgoiMBN4F9mDc\no9BeRP6ulFoS6ODqmzc3F1VUVDLuEUBkaMsoKXiSf2DKTxM5ZId3Op5J7JnPgC002GFpmtbI+HNG\nfB44Rym1EUBE+mEkiaRABhYI3txcACxRkWQXuogMtWGzNvPhGopy4buHmLF9Pn/ExjCt33UMOG5S\nsKPSNK2R8icphBQnBACl1CYRaZKjyCmPBwCx2ckucDf/qqOdi2HBrfzkTOf1dm24pOcFXKwTgqZp\n1fAnKawSkdcwblgDuIqmOiCe22hcFpuVrGxX852G05kH30+DZf8jJb47D8Qn0D+2O/884aFgR6Zp\nWiPnz1nxH8CdwH3m68XAfwMWUQCVlBSs1uY7wU7KH7DgVji8g/zjbuRu5w6shek8P+p5Qq26DUHT\ntOpVmxREZBDQE/hUKfVMw4QUOMptJAWsNrILXHRpHR7cgOqTqwB+fBz+mAGxCahrv2Ba2hck79zO\na6Nfo2Nkx2BHqGlaE1BlK6uIPIAxxMVVwHciUtkMbE2Lp7T6KLvA1XxuXEtbDq+dDH+8DMNvgAm/\nM6cwjUU7F3HbkNs4sdOJwY5Q07QmorquN1cBg5VSlwHDgQm13bmIjBGRLSKSLCL3V7PdJSKiRCSg\nPZqKq4+wWskudDf9G9dchfDdwzDrTGMCnL9/Buc+y+qsZP6z/D+c2vlUbhp8U7Cj1DStCanurFik\nlMoDUEqli0it+m6KiBVjxrYzgN3AchFZ6NuTydwuCpgI/FmryOtAmQ3NXrGQW9TEex/tWQWfTYD0\nzTDsWjjzcXBEk1GQwT0/30OHyA48OfJJLLX7tWma1sJVlxR6+MzNLEBP37malVIX17Dv4zDuft4B\nICJzgQuAjeW2ewz4N3BvbQKvE7OkUOAVoImOe+R2wq/PwOLnILIdXD0fEkcD4PK6mPzLZLKd2bw3\n+j2iQ6KDHKymaU1NdUnhknKvX67lvjsBaT6vdwPH+24gIsOALkqpL0WkyqQgIjcDNwMkJCTUMoxS\nxQ3N+R5jIrkm1/to3xr47FY4sB6GXAVnPQlhsSWrX1z5IisPrOTJk5+kT2s9MY6mabVX3SQ7PwTy\nwGZ11HPA+Jq2VUrNBGYCJCUlqRo2r3o/ZkNznsvYRZOpPvK4YPGz8Ot/IDwOrvgQ+owps8k3u77h\n7Y1vM67POMb2HBukQDVNa+oC2dK6B2PWtmKdzWXFooCBwM8iAtAeWCgi5yulVgQkIrP6KM/IDUQ3\nhZvXDmyAT/8B+9fC4MthzNMQXnZ67E2HNvHQkocY3GYw9w2/r4odaZqm1SyQZ8XlQC9zqO09wDjg\nyuKVSqksfOZ/FpGfgckBSwiUVh/lNoXqI48bfn8RfnrKqCK6/D3oV7YE4FVe3t34Li+teomY0Bie\nPfVZ7HqCHE3TjoLfSUFEQpVSRf5ur5Ryi8jtwDeAFZillNogIo8CK5RSC2sf7tEprj7KbezVR+lb\njNLB3lUw4GI4ZzpExJXZZF/uPh5c8iDL9i9jVJdRPHLCI8SFxVWxQ03TNP/4M3T2ccCbQAyQICLH\nADcqpe6o6b1KqUXAonLLplax7Sh/Aj4qZvVRjssLNNKSwpav4aNrICQCLpsNAy4qs1opxRc7vuDJ\nP5/Eq7w8euKjXJh4IWYVnKZp2lHxp6TwEnAext3NKKXWiMhpAY0qQIpvXstxK6wWISLEGuSIynHm\nw5eTIL4X/P1TiGxbZnVmYSaPLX2M71K+Y1jbYTx+8uN0iepSxc40TdNqz5+kYFFKpZS7EvUEKJ7A\nKk4KTkW0w9b4rq5/fwmy98Alb1RICD+l/sSjSx/lSNER7j72bq7tfy1WSyNLapqmNXn+JIU0swpJ\nmXcp3wFsDWxYgVHc0JzjUkQ1thvXsnbDby8Y1UVdS8cqOph/kKeXPc13Kd/Rq1UvXhv9mr4HQdO0\ngPEnKUzAqEJKAA4A31OHcZAag5KGZrdqfNNwfvcwoOCMRwGjZ9FHWz7ixVUv4vK6mDhsItcOuBa7\npZElM03TmpUaz4xKqYMY3UmbPrP6KN8LYY2pPSF1Kaz/GE65F2IT2Jq5lWl/TGNt+lpGdBjB1BFT\n6RKt2w40TQs8f3ofvQ5UuItYKXVzQCIKoJJhLtyKsIhGkhS8XvhqCkR1pHDEBP636kVmr59NVEgU\nT578JOf1OK/xtX1omtZs+VOH8r3PcwdwEWXHNGo6ioe58Ahx9kaSFNbMgX2r+WP0/Tz29d9Jy0nj\ngp4XMDlpMrGO2Jrfr2maVo/8qT760Pe1iLwL/BawiAKouKRQ4FGNo/qoKIfDP07jPwm9+GL7B3SN\n7sqbZ77JcR2OC3Zkmqa1UHVpbe0OtKvvQBpC8X0KuW5FeJBLCkopFnwzkemtbOTZ3Nwy6BZuGnyT\nnkdZ07Sg8qdNIZPSNgULcBiocha1Rs1nmItglhR2Ze3isd8fYVnmSobao3l47Pv0jO0ZtHg0TdOK\nVZsUxGjhPIbS0U29Sqk6D10dbKUNzeAIQknB5XExa/0sZq6dSSgwNeMQl1z2BhadEDRNaySqTQpK\nKSUii5RSAxsqoEBSHjdYLDi9irAGTgppOWnc+eOdJB9J5qyuZzJl/c+0ieoDXXT7gaZpjYc/E/iu\nFpGhAY+kIXg8YDWSQVhIw81dvCZ9DVcvupr0gnRe/tvLTO90Nm0ykmHEBNDdTTVNa0SqLCmIiE0p\n5QaGAstFZDuQhzFfs1JKDWugGOuNcvsmhYa5o/mHlB+YsngKbcPb8srpr9Atphu8d6kxv3L/Cxsk\nBk3TNH9Vd2ZcBgwDzm+gWAJOedylSaEBqo+2HN7Cvb/eS7/W/fjv6f+ltaM1ZCRD8ncw6gGwhQQ8\nBk3TtNqoLikIgFJqewPFEni+JYUAJ4UiTxH3L76f6JDo0oQAsOx/YLFD0nUBPb7WcrlcLnbv3k1h\nYWGwQ9GCwOFw0LlzZ+z2uo2TVl1SaCMik6paqZR6rk5HDCLl9aAsDdOm8MLKF0g+ksyM02eUJoTC\nLFj9AQy8pMLQ2JpWX3bv3k1UVBTdunXTQ6S0MEopDh06xO7du+nevXud9lHdmdEKRAJRVTyaHo8H\nVVJSCFybwu97fue9Te8xrs84Tul8SumKv94HZy6M+EfAjq1phYWFxMXF6YTQAokIcXFxR1VKrO7M\nuE8p9Wid99wIKbdvSSEw1UdHCo/w4JIH6RHTg0lJPgUtr8eoOupyPHRsHp25tMZLJ4SW62h/99WV\nFJrdX5XyuPFajI8ciDYFpRTT/phGZlEmT498mjBbWOnKbd9C5i44XpcSNE1rvKpLCqc3WBQNxe3B\nawlcQ/NnyZ/xfer33DH0DvrF9Su7cumrENUR+o2t9+NqWmOya9cuBg6s//tdCwoKOPXUU/F4SmcD\nfuGFF3A4HGRlZZUsmz17NrfffnuZ944aNYoVK1YAkJubyy233ELPnj059thjGTVqFH/++edRxaaU\n4s477yQxMZHBgwezatWqSrf78MMPGTx4MAMGDGDKlCkly1977TUGDRrEkCFDOPnkk9m4cSMA69at\nY/z48UcVW21VmRSUUocbMpCGoDwevGJ8ZEc9NzSnZafx1LKnGN5+ONf2v7bsyuTvYecvRluCVc+c\npml1MWvWLC6++GKs1tILujlz5jB8+HA++eQTv/dz44030rp1a7Zt28bKlSt56623yMjIOKrYvvrq\nK7Zt28a2bduYOXMmEyZUnJzy0KFD3Hvvvfzwww9s2LCB/fv388MPPwBw5ZVXsm7dOlavXs19993H\npElG1fOgQYPYvXs3qampRxVfbTSyOSkDy6g+Mv6gwuvx5jW31839v92PTWw8cdITWC0+pRBXISy6\nF1r31FVHWoOb9vkGNu7Nrtd99u8YzcNjB1S7jdvt5qqrrmLVqlUMGDCAd955h/DwcLp168aKFSuI\nj49nxYoVTJ48mZ9//plffvmFiRMnAkad+K+//kpUVNn+LO+//z4ffPBByevt27eTm5vLK6+8whNP\nPMF119XczXv79u38+eefvP/++1jMquTu3bvXuadOsQULFnDNNdcgIowYMYIjR46wb98+OnToULLN\njh076NWrF23atAFg9OjRzJ8/n9NPP53o6OiS7fLy8sq0C4wdO5a5c+dy3333HVWM/mq4sR4aA7cH\nT3FJwVZ/H33m2pmsTV/LQyc8RIfIDmVXLnkRDu+Ac6eDTQ+LrbUMW7Zs4dZbb2XTpk1ER0fzyiuv\nVLv99OnTmTFjBqtXr2bx4sWEhYWVWe90OtmxYwfdunUrWTZ37lzGjRvHyJEj2bJlCwcOHKgxrg0b\nNjBkyJAypY2qXH755QwZMqTC45133qmw7Z49e+jSpXTK3M6dO7Nnz54y2yQmJrJlyxZ27dqF2+3m\ns88+Iy2tdL6yGTNm0LNnT+677z5eeumlkuVJSUksXry4xnjrSwsrKRhJIcRqwWatn6Sw+uBqZq6d\nyXk9zuPs7meXXXl4Byx+FgZcDD3/Vi/H07TaqOmKPlC6dOnCSSedBMDVV1/NSy+9xOTJk6vc/qST\nTmLSpElcddVVXHzxxXTu3LnM+oyMDGJjy85EOGfOHD799FMsFguXXHIJ8+bN4/bbb6+y901te+V8\n+OGHNW9UC61ateLVV1/l8ssvx2KxcOKJJ7J9e+m9wbfddhu33XYbH3zwAY8//jhvv/02AG3btmXv\n3r31Gkt1WlRSwOPGIxYc9vpJCHmuPP65+J+0C2/HA8c/UHalUka1kTUEznqyXo6naU1F+RNw8Wub\nzYbX6wUo05f+/vvv59xzz2XRokWcdNJJfPPNN/Tt27dkfVhYWJnt161bx7Zt2zjjjDMAoyTRvXt3\nbr/9duLi4sjMzCxz/MOHDxMfH09sbCxr1qzB4/HUWFq4/PLL2bJlS4XlkyZN4pprrimzrFOnTmWu\n+nfv3k2nTp0qvHfs2LGMHWt0Npk5c2alMYwbN65Mm0RhYWGFklMgtajqI+X24BZLvd2j8PSyp9mb\nt5cnRz5JVEi5+/k2fW40MJ/2AER3qHwHmtZMpaam8scffwDwwQcfcPLJJwPQrVs3Vq5cCcD8+fNL\ntt++fTuDBg1iypQpDB8+nM2bN5fZX6tWrfB4PCWJYc6cOTzyyCPs2rWLXbt2sXfvXvbu3UtKSgrD\nhw9nyZIl7N+/H4AVK1ZQVFREly5d6NmzJ0lJSTz88MMUTw2za9cuvvzyywqf4cMPP2T16tUVHuUT\nAsD555/PO++8g1KKpUuXEhMTU6Y9odjBgwcByMzM5JVXXuHGG28EYNu2bSXbfPnll/Tq1avk9dat\nWwPSm6sqLSspeIykUB+NzN+lfMdnyZ9xw8AbOLbdsWVXFuXC1/dDu4Fw3M1HfSxNa2r69OnDjBkz\n6NevH5mZmSVXvg8//DATJ04kKSmpzFXyCy+8wMCBAxk8eDB2u52zzz67wj7PPPNMfvvNmB5+7ty5\nXHTRRWXWX3TRRcydO5d27drx4osvcs455zBkyBDuuusu5syZU9Kw/MYbb3DgwAESExMZOHAg48eP\np23boxt25pxzzqFHjx4kJiZy0003lWlDGTJkSMnziRMn0r9/f0466STuv/9+evfuDcDLL7/MgAED\nGDJkCM8991xJ1RHATz/9xLnnnntU8dWGNLWJ1JKSklRxf+Pa2nX5ODZle3hpzJ18NXFknWM4kHeA\nSz6/hE6RnXjv7Pewl+9m+u1D8PtLcP23kHB8nY+jaXWxadMm+vXrV/OGTcyqVat4/vnneffdd4Md\nSoMpKiri1FNP5bfffsNm8/9itrK/ARFZqZRKqum9La+kgIWwo2hT8CovDy15CKfHydMjn66YEA5s\nhKWvwNC/64SgafVo2LBhnHbaaWVuXmvuUlNTefrpp2uVEI5WQJOCiIwRkS0ikiwi91eyfpKIbBSR\ntSLyg4h0DWQ8yuPBhRxVm8L7m97nj31/MDlpMt1jyvVtVgq+vAdCo2D0tKOMVtO08q6//nq/upM2\nF7169WLUqFENesyAJQURsQIzgLOB/sAVItK/3GZ/AUlKqcHAx8AzgYoHALcbF5Y6j5C6NXMrL6x8\ngVGdR3FZ78sqbrBmLqT+biSEiLijDFbTNK3hBbKkcByQrJTaoZRyAnOBC3w3UEr9pJTKN18uBToT\nQMrrxV3HkkLxpDmRIZE8cuIjFfs8F2TCdw9B5+FG1ZGmaVoTFMik0AlI83m921xWlRuArypbISI3\ni8gKEVmRnp5e94jcbpxIndoUXlz1Itsyt/HYSY8RF1ZJKeDHxyH/EJz7LFhaVFONpmnNSKM4e4nI\n1UAS8J/K1iulZiqlkpRSScXjhtSF8nhwKkutR0j9fe/vvLvx3YqT5hTbswqWv2l0P+1wTJ3j0zRN\nC7ZAJoU9QBef153NZWWIyGjgX8D5SqmiAMZjJAUERy2qj7KKsnjot4foHtO97KQ5xbwe+HKSMb3m\naQ9UXK9pWp2NHz+ejz/+uNJ1d911F7/++mvJ64yMDOx2O6+99lqZ7SIjI8u8Lj+09jvvvMPAgQMZ\nNGgQQ4cOZfr06Ucd99dff02fPn1ITEzk6aefrnSbu+++u2Q8pd69e5cZxiM1NZUzzzyTfv360b9/\nf3bt2gUYdzv73ugWCIFMCsuBXiLSXURCgHHAQt8NRGQo8D+MhHAwgLEAoNxuXEoIr0VD80dbPuJg\nwcGKk+YUW/kW7P3LGMrCEVOP0WqaVpVDhw6xdOlSTjmltOQ+b948RowYwZw5c/zez1dffcULL7zA\nt99+y7p160ruRj4aHo+H2267ja+++oqNGzcyZ86ckvkRfD3//PMld0nfcccdXHzxxSXrrrnmGu69\n9142bdrEsmXLSm6umzBhAs88E9j+OAHr/KqUcovI7cA3GPM9z1JKbRCRR4EVSqmFGNVFkcA8s+E2\nVSl1fsBicnvwWCxE1mIuhUU7FzG07VD6x5XvOAXkHoTvH4Xup8DAS+oxUk2rJ1/dD/vX1e8+2w+C\nsyu/+i323HPPMWvWLLZ9/sAAABzaSURBVMCYv+Cuu+5i165djBkzhmOPPbbCkNorV65k0qRJ5Obm\nEh8fz+zZsysdJqLY/PnzGTNmTJllc+bM4dlnn+XKK69k9+7dFQbVq8xTTz3F9OnT6dixIwChoaHc\ndNNNNb6vOsuWLSMxMZEePXoAxtX9ggUL6N+/knOIT+zTphnd2Ddu3Ijb7S4Z18m3pDNy5EjGjx+P\n2+0O2L0LAW1TUEotUkr1Vkr1VEo9YS6baiYElFKjlVLtlFJDzEfAEgIY8yl4xOp3m8K2zG0kH0mu\nOPppse+mgisfznkW9Jy4mgZQMnHNn3/++f/tnXlcVdX6/98PCIKlaWqpoKKiqEggDnjLMrVBzSFN\nIa+WXrP71aJrZaZl3Z+/7Nc1K7NRU+uq5YWMnDLQryVeR0qxMDQTEjQUZ5yJyfX7Y2+2DAc5CAeE\ns96v13m9zl5r7bWetQ/sZ69hfx7i4uJYuHAhP/30E2BbUjsnJ4dnnnmGqKgo4uPjGTduHNOnT79m\nG9u2baNLl6vyMn/88Qfp6el0796d0NBQuxVOExMTC9VTEsuWLbMpoz18+PBiZe2R0S7IoUOHSElJ\noU8fQ0n5wIED1K9fn2HDhtG5c2emTJlivbDn4uKCr68vCQkJdvXvenAqlVRlxlPwsNMpxKTE4CIu\n3N/y/uKZqVshIQLungyN21WwpRpNBVHKE70j2Lp1K0OHDuWmm24CYNiwYWzZsoXBgwfblNTu168f\niYmJ1pNxXl7eNUcJAOnp6RTcdPLll18SGhoKGE/m48aNY/LkySWeX1YZ7VGjRjFq1KgynWMvkZGR\nDB8+3HopLzc3ly1btvDTTz/RokULwsLCWLx4MU888QRwVUrbHmd2PTiVU8iXzrbnPQWlFDEpMYQ0\nCaGRZ6Mi9eQYby7f0gLuLlkjXqPRFMaWpLZSCn9/f0tV1R6KSmlHRERw7Ngxli1bBsDRo0dJSkqi\nbdu2eHp6kp2djbu7O3BVRhvA39+f+Ph46ym9JJYtW8ZbbxXfHOnr61tsIdxeGe18IiMj+eijj6xj\nb29vgoKCrOmnhx9+mLi4OMspOFpK+4bYklpZqDxjTaGOHU4h8VQiaRfTbE8dxX0MJ/fDgNngXscB\nlmo01Ze7776bVatWcfnyZS5dusTKlSu5+25DgNKWpLafnx8nT5600nNycti7d+812+jQoQPJycmA\nMd1y8eJFjhw5Yklpv/TSS9aCc69evfjiiy8AyMzMZPny5fTu3RuAl156iSlTplgy29nZ2SxatKhY\ne6NGjbIpo21rZ1S3bt1ISkoiJSWF7OxsIiMjGTzY9sz4/v37ycjI4C9/+Uuh88+ePUv+O1kbN24s\ntB7haCltp3IK5OVxxc7po5jUGNxc3Ojbsm/hjHNpsOlN8BsAfiWsNWg0TkxwcDBjx46le/fuhISE\nMH78eDp37gzYltR2d3cnKiqKqVOnEhgYSFBQENu3b79mGw899BCbNm0CjFFCURntRx55xHIK7733\nHitWrCAoKIgePXowYsQIa9fSgAEDCA8P57777sPf35/g4GDOny9fTOtatWrx4Ycf8uCDD9KhQwdC\nQ0Px9zci4P3zn/9kzZqrmzDzQ4oWHEG5urry9ttv07dvXwICAlBKWYvfx48fx9PTkyZNmpTLxmvh\nNNLZ6soV9nf05/P2DzDi/Rl0btGgxLJ5V/J4IOoB/Bv5836f9wtnfvkYJG2Ap3+ABg7V79Norosb\nVTo7NTWVgQMHkpiYWCH19ezZk7Vr1xYL01mTeffdd6lXr541lVQSWjrbHnJzAYzdR6VMH+0+sZsT\nmScY0GpA4YykDfDrGrjnBe0QNJoq5p133uHw4cNVbUalUr9+fcaMGePQNpxmoVmZW7quiJS6JTU6\nJRrPWp6FJS1yMiH6BWjYFu58xpGmajQ1Eh8fnwobJQCEhDhfvJK//e1vDm/D6ZxCabuPcq7ksOHQ\nBu5tfi913AosIm+dCxmp8PhqqFXbwdZqNBpN1eB800cu1355bcfRHZzLOld46uj077D1Xeg0HFrf\n61g7NRqNpgpxGqegrlwBKPXltZiUGOq61+WuZneZJyqIngKu7vDg/6sMUzUajabKcB6nYI4UcHXF\nzdV2t//M/ZONhzdyf8v7r8Ze3rcafv8e+rwCdR23DUyj0WhuBJzGKWCuKbheQ0Rqc9pmLudevvrC\nWtYFWPeSIQDWbXxlWKnRaEyuJZtdkPT0dAYOHFgo7dlnn8XLy4sr5gwBwIwZM4rJYvv4+HDq1CkA\njh07xqOPPkqbNm3o0qULAwYM4MCBA+XqQ1ZWFmFhYfj6+hISEmJJYBfkt99+K6SnVK9ePebOnWvl\nf/DBB7Rv3x5/f39efPFFAH755RfGjh1bLttKwukWml3dS+5yTEoMjTwb0e32bkbCpllw4SiELgVX\np7lUGk21Ys6cOYWUTa9cucLKlStp3rw5//3vf623l6+FUoqhQ4cyZswYIiMjAUhISOD48eO0a3f9\n2maffvopDRo0IDk5mcjISKZOnVpMrM/Pz4+ff/4ZMHSfvLy8rJfxYmNjWb16NQkJCdSuXZsTJ4wI\nAwEBAaSlpXH48GFatGhx3fbZwnnudOb0UUkjhYvZF9mctpkRfiNwdXGF4/sgbh4Ej4Hm3SrTUo2m\nwnjzxzfZf2Z/hdbZ/tb2TO0+tcT8S5cuERoaSlpaGnl5ebz66quEhYXx2muv8c0335CZmcmdd97J\nJ598goiwcOFCFixYQHZ2Nr6+vnz++efUqWPs/Pvuu++YNWsW58+fZ86cOcVGBGDIaL/++uvW8aZN\nm/D39ycsLIyIiAi7nEJsbCxubm5MmDDBSgsMLH8UxdWrVzNjxgwAhg8fTnh4OEqpEgX5vv/+e9q0\naUPLlsZ7UPPmzWPatGnUrm3seMyPqwAwaNAgIiMjrdFDReE000fWSKGEADsb/9hI9pVs+vn0MxaX\nv33eCJpz34zKM1KjqQGsW7eOZs2akZCQQGJiohX3IDw8nJ07d5KYmEhmZiZr164FDBXVnTt3kpCQ\nQIcOHfj000+tulJTU/nxxx/59ttvmTBhQiERPICUlBQaNGhg3TTBkL0YOXIkQ4cO5dtvvyUnJ6dU\nm+2V0AZD28mWjPZ3331XrGxBGe1atWpxyy23cPr06RLrjoyMZOTIkdbxgQMH2LJlCyEhIfTq1Yud\nO3daeV27dmXLli122VwWnGakoHINp1DL3c1mfnRKNF43exHYONCQxD68AwZ/CHVurUwzNZoK5VpP\n9I4iICCAyZMnM3XqVAYOHGiJ4cXGxjJ79mwuX77MmTNn8Pf3Z9CgQSQmJvLKK69w9uxZLl68yIMP\nPmjVFRoaiouLC23btqV169bs37+foKAgK7+ohHZ2djbR0dHMmTOHunXrEhISwvr16xk4cGCJT+dl\nldF2xI0YDNvXrFnDv/71LystNzeXM2fOEBcXx86dOwkNDeXgwYOIiCWhXdE4jVMgz5g+crOxppDx\nZwZxR+MY6z8WycyA/30VmodAkGP00zWamky7du3YvXs30dHRvPLKK/Tt25cXX3yRp556il27dtG8\neXNmzJhhPfWPHTuWVatWERgYyOLFiy2hO7AttV2QohLa69ev5+zZswQEBABw+fJlPD09GThwIA0b\nNiQ9Pb3Q+RcuXKB+/fr4+/vbtagNxkjhwoULxdLffvtt7rvvvkJp+TLa3t7e5Obmcu7cORo2bGiz\n3piYGIKDg7n99tutNG9vb4YNG4aI0L17d1xcXDh16hSNGzd2mIS2000f1XIrPlLYcGgDeSrP2HW0\ncSZknoGH3gEXp7k8Gk2FcfToUerUqcPo0aOZMmUKu3fvtm7cjRo14uLFi4VuwBcuXKBp06bk5ORY\n8RDy+eqrr7hy5Qq///47Bw8exM/Pr1B+u3btCu3oiYiIYNGiRZaEdkpKChs2bODy5cvcc889rFmz\nxrqhr1ixgsDAQFxdXenTpw9ZWVksWLDAqmvPnj02RwVbtmyxKaNd1CEADB48mCVLlgAQFRVFnz59\nShyZ5E97FeThhx8mNjYWMKaSsrOzrVgQjpLQdpqRQv57CracQnRKNK1vaU27Sxdg17+hx0RjG6pG\noykzv/zyC1OmTMHFxQU3NzfmzZtH/fr1efLJJ+nUqRNNmjShW7ermzdmzpxJSEgIjRs3JiQkpNBT\neIsWLejevTvnz59n/vz5eHh4FGrrpptuok2bNiQnJ9OsWTPWrVvH/PnzC+X37NmTb775hrCwMMLD\nw+nZs6c1/ZIfO0FEWLlyJc8++yxvvvkmHh4e+Pj4FNoaej088cQTPPbYY/j6+nLrrbdaO5uOHj3K\n+PHjiY6OBozF+Q0bNvDJJ58UOn/cuHGMGzeOTp064e7uzpIlSyynEhsby0MPPVQu+2zhNNLZl3ft\n4tDox4gZO53np4220o9dOsYDUQ/wVOBEJvwQAReOQ/hO8KhXkWZrNJXGjSqd7ShWrlxJfHx8oR1I\nNZ2srCx69erF1q1bqWVjR2V5pLOdaKRgTB+51S48Ulifuh6Fov/Fi5CeAMM/0w5Bo6lGDB069Jo7\nemoihw8fZtasWTYdQnlxHqdgLjS7F9l9FJMSQ8f67Wi55QND7M5/WOUbp9FoysX48c6lONC2bVva\ntm3rkLqdZiX1Sk5xp3Do/CH2nt7LgMwsyM2EAe9AGbenaTQaTU3CaZxCdrbxAktBp7AuZR0ADyZt\nh7smQSPfKrFNo9FobhScxylkmU7Bw3AKSiliUqIJznOhSV1vuHtyVZqn0Wg0NwRO4xSyiowUDmQc\n4PdzBxmQcQr6zwa3in8JRKPRaKobTuMU8kcKHh7uAKzbvxxXpbi/WU/w61eVpmk0GjuZO3cuS5cu\ntY5zc3Np3Lgx06ZNK1SuoCQ2GCJ5BcX0YmJi6Nq1Kx07dqRz585Mnlz+mYL4+HgCAgLw9fXlH//4\nB7a2+2dkZDB06FDuuOMOunfvbsWsvpZ89gsvvMDGjRvLbZ+9OI9TMEcKtWu7GVNHSavokZXDrf3f\nqmLLNBqNPeTm5vLZZ5/x17/+1UrbsGED7dq146uvvrJ5E7ZFYmIi4eHhfPHFF+zbt49du3bh61v+\n9cSJEyeycOFCkpKSSEpKYt26dcXKvPHGGwQFBbFnzx6WLl3KpEmTgKvy2T///DPx8fHUqVPHks9+\n5plnmDVrVrntsxen2ZKak5VDLcCjtjt7di/kCNlMbN4X6lesFrlGcyNx7I03yPq1YqWza3doT5OX\nXy4xf9q0aTRv3pynn34aMILb3HzzzUyYMIEhQ4aQkZFBTk4Or7/+OkOGDCE1NZX+/fvTs2dPtm/f\njpeXF6tXry6m67Nx40aCg4ML7c2PiIhg0qRJzJs3jx07dnDnnXeWav/s2bOZPn067du3B8DV1ZWJ\nEydez6WwSE9P5/z58/To0QOAxx9/nFWrVtG/f/9C5fbt22eNatq3b09qairHjx8vpHdUVD67ZcuW\nnD59mmPHjtGkieOjPzrNSCEnf6TgpojZ9QHuCvrcO7OKrdJoah5hYWEsX77cOl6+fDlhYWF4eHiw\ncuVKdu/eTWxsLJMnT7ae7pOSknj66afZu3cv9evX5+uvvy5W77Zt2wrJW//555989913DBo0iJEj\nRxIREWGXffbKZMfGxtqUyLbleI4cOYK3t7d17O3tzZEjR4qVCwwMZMWKFQD8+OOPHDp0iLS0tEJl\nispnAwQHB7Nt2za7+ldenGekYDqFJkmfs75WHnc3CqZuHdtqhRpNTeFaT/SOonPnzpw4cYKjR49y\n8uRJGjRoQPPmzcnJyeHll19m8+bNuLi4cOTIEY4fPw5Aq1atLEnsLl262AxbmZ6eXki6Ye3atfTu\n3RtPT08eeeQRZs6cydy5c3F1dbUpOldWiezevXtbEdEqimnTpjFp0iSCgoIICAigc+fOuLq6Wvm2\n5LMBh8lk28KhTkFE+gHvAa7AIqXUrCL5tYGlQBfgNBCmlEp1hC35TuHw7xGcatmA/p0ec0QzGo0G\nGDFiBFFRURw7doywsDAAli1bxsmTJ4mPj8fNzQ0fHx9LPbVgkBxXV1cyMzOL1VlUJjsiIoKtW7fi\n4+MDwOnTp9m4cSP3338/DRs2JCMjw1IUPXPmjPXd39+f+Pj4UiOrxcbG8txzzxVLr1OnDtu3by+U\n5uXlVeiJPy0tDS8vr2Ln1qtXj3//+9+AsS2+VatWtG7d2sq3JZ8NOEwm2xYOmz4SEVfgI6A/0BEY\nKSIdixR7AshQSvkC7wJvOsqefKewoW4d6tTypJd3L0c1pdE4PWFhYURGRhIVFcWIESMAOHfuHLfd\ndhtubm7ExsZy6NChMtXZoUMHkpOTATh//jxbtmzh8OHDlkz2Rx99ZE0h3XvvvXz++eeAEff4iy++\nsMJyTpkyhTfeeIMDBw4ARkzngsqq+eSPFIp+ijoEgKZNm1KvXj3i4uJQSrF06VKGDBlSrNzZs2fJ\nzs4GYNGiRdxzzz3Uq3dVa82WfDY4TibbFo5cU+gOJCulDiqlsoFIoOhVGgIsMb9HAX2lrGM8Ozl7\nahMA0XU96dOiLx61PK59gkajuW78/f25cOECXl5eNG3aFIBRo0axa9cuAgICWLp0qbXQay/9+/dn\n8+bNgKGM2qdPn0IjjCFDhvDNN9+QlZXFq6++SnJyMoGBgXTu3BlfX19GjzbUke+44w7mzp3LyJEj\n6dChA506deLgwYPl7vPHH3/M+PHj8fX1pU2bNtYi8/z58y2n8+uvv9KpUyf8/PyIiYnhvffes87P\nl88eNqyw/lpOTg7Jycl07VqqwGmF4DDpbBEZDvRTSo03jx8DQpRS4QXKJJpl0szj380yp4rU9Xfg\n7wAtWrToUtYnDICvZ/0Prtu2s+2ZvjzZ7SnaNWh3vV3TaG5oarJ09tChQ5k9e7bDxOBuRPIX52fO\ntH9jTI2XzlZKLQAWgBFP4XrqeGSaEbzi4YozS6PRVDKzZs0iPT3dqZxCbm5uhbxcZy+OdApHgOYF\njr3NNFtl0kSkFnALxoKzRqPRFMPPz69YSM6aTv6aTGXhyDWFnUBbEWklIu7Ao8CaImXWAGPM78OB\njaq6hYLTaG5A9L+R81Le395hTkEplQuEA+uBX4HlSqm9IvKaiAw2i30KNBSRZOB5YJrt2jQajb14\neHhw+vRp7RicEKUUp0+fLhbLuiw4TYxmjcZZyMnJIS0trdCefo3z4OHhgbe3N25uhaNM1qiFZo1G\nYz9ubm60atWqqs3QVFOcRvtIo9FoNKWjnYJGo9FoLLRT0Gg0Go1FtVtoFpGTQNlfaTZoBJwqtVTN\nQvfZOdB9dg7K0+eWSqnGpRWqdk6hPIjILntW32sSus/Oge6zc1AZfdbTRxqNRqOx0E5Bo9FoNBbO\n5hQWVLUBVYDus3Og++wcOLzPTrWmoNFoNJpr42wjBY1Go9FcA+0UNBqNRmNRI52CiPQTkd9EJFlE\niimvikhtEfnSzP9BRHwq38qKxY4+Py8i+0Rkj4h8LyItq8LOiqS0Phco94iIKBGp9tsX7emziISa\nv/VeEflPZdtY0djxt91CRGJF5Cfz73tAVdhZUYjIZyJywoxMaStfROR983rsEZHgCjVAKVWjPoAr\n8DvQGnAHEoCORco8Bcw3vz8KfFnVdldCn3sDdczvE52hz2a5usBmIA7oWtV2V8Lv3Bb4CWhgHt9W\n1XZXQp8XABPN7x2B1Kq2u5x9vgcIBhJLyB8AxAAC9AB+qMj2a+JIoTuQrJQ6qJTKBiKBIUXKDAGW\nmN+jgL4iIpVoY0VTap+VUrFKqcvmYRxGJLzqjD2/M8BM4E2gJuhI29PnJ4GPlFIZAEqpE5VsY0Vj\nT58VUM/8fgtwtBLtq3CUUpuBM9coMgRYqgzigPoi0rSi2q+JTsEL+KPAcZqZZrOMMoIBnQMaVop1\njsGePhfkCYwnjepMqX02h9XNlVLfVqZhDsSe37kd0E5EtolInIj0qzTrHIM9fZ4BjBaRNCAaeKZy\nTKsyyvr/XiZ0PAUnQ0RGA12BXlVtiyMRERdgDjC2ik2pbGphTCHdizEa3CwiAUqps1VqlWMZCSxW\nSr0jIn8BPheRTkqpK1VtWHWkJo4UjgDNCxx7m2k2y4hILYwh5+lKsc4x2NNnROQ+YDowWCmVVUm2\nOYrS+lwX6ARsEpFUjLnXNdV8sdme3zkNWKOUylFKpQAHMJxEdcWePj8BLAdQSu0APDCE42oqdv2/\nXy810SnsBNqKSCsRccdYSF5TpMwaYIz5fTiwUZkrONWUUvssIp2BTzAcQnWfZ4ZS+qyUOqeUaqSU\n8lFK+WCsowxWSlXnWK72/G2vwhglICKNMKaTDlamkRWMPX0+DPQFEJEOGE7hZKVaWbmsAR43dyH1\nAM4ppdIrqvIaN32klMoVkXBgPcbOhc+UUntF5DVgl1JqDfApxhAzGWNB59Gqs7j82Nnnt4Cbga/M\nNfXDSqnBVWZ0ObGzzzUKO/u8HnhARPYBecAUpVS1HQXb2efJwEIReQ5j0XlsdX7IE5EIDMfeyFwn\n+T+AG4BSaj7GuskAIBm4DPytQtuvxtdOo9FoNBVMTZw+0mg0Gs11op2CRqPRaCy0U9BoNBqNhXYK\nGo1Go7HQTkGj0Wg0FtopaG44RCRPRH4u8PG5RlmfktQky9jmJlOJM8GUiPC7jjomiMjj5vexItKs\nQN4iEelYwXbuFJEgO855VkTqlLdtjXOgnYLmRiRTKRVU4JNaSe2OUkoFYoglvlXWk5VS85VSS83D\nsUCzAnnjlVL7KsTKq3Z+jH12Pgtop6CxC+0UNNUCc0SwRUR2m587bZTxF5EfzdHFHhFpa6aPLpD+\niYi4ltLcZsDXPLevqdP/i6lzX9tMnyVX41O8babNEJEXRGQ4hr7UMrNNT/MJv6s5mrBu5OaI4sPr\ntHMHBYTQRGSeiOwSI47C/zXT/oHhnGJFJNZMe0BEdpjX8SsRubmUdjROhHYKmhsRzwJTRyvNtBPA\n/UqpYCAMeN/GeROA95RSQRg35TRT9iAMuMtMzwNGldL+IOAXEfEAFgNhSqkADAWAiSLSEBgK+Cul\n7gBeL3iyUioK2IXxRB+klMoskP21eW4+YUDkddrZD0PWIp/pSqmuwB1ALxG5Qyn1PoaUdG+lVG9T\n+uIV4D7zWu4Cni+lHY0TUeNkLjQ1gkzzxlgQN+BDcw49D0PTpyg7gOki4g2sUEoliUhfoAuw05T3\n8MRwMLZYJiKZQCqG/LIfkKKUOmDmLwGeBj7EiM/wqYisBdba2zGl1EkROWhq1iQB7YFtZr1lsdMd\nQ7ak4HUKFZG/Y/xfN8UIOLOnyLk9zPRtZjvuGNdNowG0U9BUH54DjgOBGCPcYkFzlFL/EZEfgIeA\naBH5H4zoVEuUUi/Z0caogoJ5InKrrUKmHk93DBG24UA40KcMfYkEQoH9wEqllBLjDm23nUA8xnrC\nB8AwEWkFvAB0U0pliMhiDGG4ogiwQSk1sgz2apwIPX2kqS7cAqSbGvmPYYijFUJEWgMHzSmT1RjT\nKN8Dw0XkNrPMrWJ/fOrfAB8R8TWPHwP+a87B36KUisZwVoE2zr2AId9ti5UY0bNGYjgIymqnKfj2\nKtBDRNpjRB67BJwTkduB/iXYEgfcld8nEblJRGyNujROinYKmurCx8AYEUnAmHK5ZKNMKJAoIj9j\nxFJYau74eQX4XxHZA2zAmFopFaXUnxgKlF+JyC/AFWA+xg12rVnfVmzPyS8G5ucvNBepNwP4FWip\nlPrRTCuzneZaxTsYSqgJGLGZ9wP/wZiSymcBsE5EYpVSJzF2RkWY7ezAuJ4aDaBVUjUajUZTAD1S\n0Gg0Go2FdgoajUajsdBOQaPRaDQW2iloNBqNxkI7BY1Go9FYaKeg0Wg0GgvtFDQajUZj8f8BV6pq\nllzG5n4AAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Исходные данные\n",
            "Лучший параметр: {'max_depth': 5}\n",
            "score: 0.622895622895623\n",
            "weighted f1 score: 0.6323988176118319\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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w80Idjqa1G4HsfbQD8D18SzKn+boCOAlAKfWriIQDCcDuAMZVr+rqo9gIG5Wb\njJ5HDn2m0Lp4vZD9O2R8zBvb5hERYedPp7wQ6qg0rV0JZFJYAvQXkd4YyeAC4KI6y2QBxwOvi8gg\nIBwITP1QE4or3IBRfVSxeRNit+NI1lUSIef1QvZvsHYerJsPJbnssUfwaVI3zks9ibiYXqGOUNPa\nlYAlBaWUW0SuBr4ArMCrSqm1InIvsFQpNR+4CXhZRG7AaHSeppRSgYqpMcVOFxaBKIeNfZs24+jd\nG7HpyzhCwuuBrF/NRLAASvPAFg79JsHgM5lTuR1Pxmymjq63CUrTtEMQ0FJPKbUQowHZd9qdPq8z\ngAmBjMFfRRUuYsLtWCxC5aZNRAwfFuqQOqaSPHhlMuzLMhJB/8kw+EwYcCKExVDhruDd9ydzXMpx\nJMfqMzlNa2n6UNhUXOEiLsKOt7wc144dxJ11ZqhD6phWzjESwpkvwKApEBZda/aCzQsoqiwyRlbT\nNK3F6aRgKna6jUbmrVtBKX2NQqisfh+SDoeRFx4wy6u8vJHxBkO7DGVUt1EhCE7T2r9Qd0ltNYor\nXEZ31M1GzyPdHTUEdmXArjXGXU7r8WPOj2wr3sZlQy5D9A3vNC0gdFIwFZlJoXLTZrDZcKSmhjqk\njmf1eyBWGHJWvbNnZ8ymZ1RPJqVOCnJgmtZx6KRgKnYabQqVmzfjSEtF7HrchKDyeo2qo77HQvSB\nFyhmFGSwJG8JFw+6GJtF13pqWqDopGAqrjDaFKo26XsehUTOYijKarDq6I2MN4iyR3F2/7ODHJim\ndSw6KQBVbi8VLg9xVqjKzsbRp3eoQ+p4Vr8Htgg47NQDZu0q28XnWz/n7P5nE+OICUFwmtZx6KTA\n/vsedSvfC14vYWlpoQ2oo/G4YO1HMPBkCDuw0H97/dt48XLxoItDEJymdSw6KbD/DqmdCncBYE9J\nCWU4Hc/m76C8oN6qo3JXOe9teI9JKZNIjK57k11N01qaX0lBRBwi0m4r2qtvhhezNw9A9zwKttXv\nQXi8cRuLOuZtmkdJVQmXDtEXq2laMDSZFETkVGA18JX5fqSIfBTowIKp2GncDC9yTy6W6GisnTqF\nOKIOpKoM1n8KQ84Em6PWLI/Xw5vr3mRE1xGM6DoiRAFqWsfiz5nCvcA4YB+AUmoF0K7OGqqrjxx5\nO3GkpOgLo4Ip8zNwldVbdfR99vdkl2Rz2ZDLQhCYpnVM/iQFl1JqX51pIbmTaaBUVx9Jbg72VN2e\nEFSr34PYREg58oBZszNmkxidyHHJx4UgME3rmPxJCutE5DzAIiK9ReRJ4LcAxxVUxU4XVq8Hb24u\njhTdnhA0ZQWw6WsYeg5Yav9+jk6cAAAgAElEQVQUV+9ZzfLdy5k6aCpWPfaypgWNP0nhamAM4AU+\nBCqB6wIZVLAVV7hJrCwCtxuH7nkUPBnzwOuut+rojYw3iLHHcFb/+m95oWlaYPiTFE5USs1QSo0y\nHzOBkwMdWDAVVbjo6yoEwKGrj4Jn9fvQ9TDoUXvsitzSXL7c/iV/GvAnouxRIQpO0zomf5LC7fVM\nu62lAwmlYqeLVOdeQF+jEDT7siDrFxj2J6jTsP/WurcAuGhQ3dFbNU0LtAbvLCYiJwInAYki8oTP\nrFiMqqR2o7jCxcjyAiQiAlvXA2/GpgXAmg+M5zpVR6VVpXyw8QNOSDuBHlE9QhCYpnVsjd1ucjew\nBnACa32mlwAzAxlUsBVXuOhekq+7owZT9WA6ndJqTf5o00eUukq5bLDuhqppodBgUlBK/QH8ISJv\nKaWcQYwp6IqdbhKKduMYNSTUoXQM1YPpnPxorclur5u31r3F6G6jGZKg/xaaFgr+tCkkishcEVkl\nIhuqHwGPLIhKyiqJLdytG5mDpYHBdL7J+oYdpTv0xWqaFkL+JIXXgdcAweh19C7wTgBjCiqlFI69\ne7B63LqRORiUanAwndkZs0mJSeGYpGNCFJymaf4khUil1BcASqnNSqnbaUddUitcHrqV7AHQF64F\nQ/bv5mA659Wa/Fvub6zas4qpg/XFapoWSv6Ma1gpIhZgs4j8HdgBtJuRToor3PQqywf0NQpBUTOY\nzik1k9xeNw8vfpjE6EQ9spqmhZg/SeEGIAq4FrgfiAP+HMiggqmowkWvsgK8dge27t1DHU77Vmcw\nnV1lu1i6aynfZH3Dpn2beGriU4RZw0IdpaZ1aE0mBaXU7+bLEuASABFpN6OdFDtd9CzNx9szEbHo\nMYcCKTfjQ5ZaKlgaJSz98FSySrIAiLZHc+FhF3Jcir7xnaaFWqNJQUTGAonAT0qpfBEZAswAjgOS\nghBfwBVXuOhVlo+lz8BQh9LuVHmq+GzrZyzJW8LSXUvZUboDuiYQW5TJ6O6jOX/g+aT3SGdgp4G6\nHUHTWonGrmh+EDgHWAncLiKfAFcBDwN/D054gVdUVkmfsgLd8ygAHlz8IO9veJ/4sHjGdB3B1JwN\njE2ZSP8z/4tF9FmZprVGjZ0pnAGMUEpViEhnIBsYppTaEpzQgsOZu4swr5vI3rrnUUtav3c9H2z4\ngIsHXcwtY2/BsuZDKHwDzrgCdELQtFarsf9Op1KqAkAptRfY0N4SAoAnJxuAmL69QxxJ+6GU4qHF\nDxEfFs9VI68yzgpWvwexSZByRKjD0zStEY0lhT4i8qH5+Ajo7fP+Q39WLiIniUimiGwSkXrvlyQi\n54lIhoisFZG3D2YnDsnOHAAi0tKCvun26svtX7Js1zKuGX0NsY7Y/YPpDDtwMB1N01qXxqqPzqnz\n/j/NWbGIWIFZwGQgB1giIvOVUhk+y/QHbgUmKKUKRaRbc7bREhx5O3BbrNh76jtytgSn28njSx9n\nYKeBnN3PvOagkcF0NE1rXRq7Id43h7juw4FN1VVOIjIXo50iw2eZvwKzlFKF5jZ3H+I2my1iTy4F\nsQmIVfd+aQmvr32d3LJc7j/q/v09iqoH0+k+NLTBaZrWpECeyydiNE5XyzGn+RoADBCRn0XkNxE5\nqb4ViciVIrJURJbu2bOnRYOMKchjXyd90VpLyCvL45XVrzA5dTJje4w1Jm7+1hhMZ8SFBwymo2la\n6xPqCl4b0B+YCFwIvCwi8XUXUkq9pJRKV0qld23BQXCUUnQq3E1pF111dKiUUjz4+4N4lZeb0m8y\nJlaVwYLroEt/GNduejFrWrvmd1IQkebef2AHkOzzPsmc5isHmK+UcimltgIbMJJEUHjy8wlzV+Ls\n1itYm2y3/rf2f3yb/S3Xjr6WxGjzhPDbfxvDbp7+LNjDQxugpml+aTIpiMjhIrIa2Gi+HyEiz/qx\n7iVAfxHpLSIO4AJgfp1l5mGcJSAiCRjVSUHr9lqVZdxmwd2z3dy1IyQW5y7myeVPMjl1MpcOvtSY\nmL0Efnsexv4VUnU3VE1rK/w5U3gGOA0oAFBKrQSObepDSik3cDXwBbAOeFcptVZE7hWR083FvgAK\nRCQD+A64WSlV0PzdODiV27YbLxKTG19Qa1BeWR43/3gzqbGp3DfhPmM4U3clzL8aYhNh0l2hDlHT\ntGbw5y6pFqXU9jpjF3v8WblSaiGwsM60O31eK+BG8xF0pVu34REL9p49Q7H5Ns/lcXHTDzfhdDt5\n7cTXiLJHGTMWPQF71sNF70FYu7nLuqZ1CP4khWwRORxQ5rUH12DU/bd5zq3b2RXZiZiYiFCH0iY9\nsuQRVu1ZxePHPE6f+D7GxF1rYdHjMPx8GHBCaAPUNK3Z/Kk++gfGkXwKsAsYb05r81zZWeyMSiA2\n3B7qUNqcBZsXMDdzLtOGTOOENLPw93pg/jUQHgsnPhjaADVNOyj+nCm4lVIXBDySIFNKoXbksLPb\ncPpF6KTQHJl7M7n313sZ22Ms142+bv+M31+AHcvgnFcgqkvoAtQ07aD5c6awREQWishlItJuKog9\n+/ZhKSslNyqB2Ah/cqMGUFRZxPXfXU+sI5ZH/u8RbBbzu9u7Fb65DwacBEPr3iFF07S2osmkoJTq\nC/wbGAOsFpF5ItLmzxxc242eRzujdfWRv7zKy20/3UZeeR6PT3ychIgEY4ZSsOBasNjg1Cf0lcua\n1ob5dfGaUuoXpdS1wGigGHgroFEFQfU1CjujEoiL1EnBHy+vepkfcn7glrG3MLLbyP0z/ngTtv4I\nJ9wLcfqaD01ry/y5eC1aRC4WkQXAYmAPcGTAIwuwqu1ZKBF2RXUm2qGrj5ry846fmbViFqf1OY0L\nBvqcKBbnwhe3QeoEGD0tZPFpmtYy/CkN1wALgEeUUosCHE/QVGVlUR6fQERkOBaLru5ozI7SHcxY\nNIP+nfpz5xF3UuualYXTwVMJU57RYyVoWjvgT1Loo5TyBjySIKvK2k5hp+666qgJlZ5KbvjuBrxe\nL09OfJIIm881HRkfw/pPYNLdkNAvVCFqmtaCGkwKIvK4Uuom4AMRUXXnK6XODmhkAebatp383qN1\nI3MTHvj9AdbtXcezxz1LSmzK/hnle+HT6dBzBBxxTegC1DStRTV2pvCO+dysEdfaAs++fXiKisiN\n6aqTQiM+2PABH278kCuHX8nE5Im1Z355O5QXwNQPwKrbZDStvWhs5LXF5stBSqlaiUFErgYOdWS2\nkKnKNsb+yYnsTJy+cK1ea/LXcP/v93NkryO5asRV+2cUboOs32DFW3DUjdBzeMhi1DSt5fnTMvjn\neqZd0dKBBFPVdqM76tawzvrCtXoUOgu58fsb6RrRlYePftgYVrOyFD65EZ4eAR/9Dbr0g2NmhDpU\nTdNaWGNtCudjjIHQW0Q+9JkVA+wLdGCBVJVlXLi2xRbHGF19VIvH62HGjzMoqChg9smziQ+Ph20/\nwbyrjAFzxl8FaUdByhF64BxNa4caO0xejDGGQhIwy2d6CfBHIIMKNNf2LKzde1DkterqozpmrZjF\nr7m/cs+R9zAktjd8NsO4p1HnPnD5Z3rAHE1r5xprU9gKbAW+Dl44wVGVlYUlKQmAWJ0UanyX9R0v\nr36Zc/qfw9lhveCFCbB3izG+8vF3giMq1CFqmhZgjVUf/aCUOkZECgHfLqmCMT5O54BHFyBVWVmo\nI44C0G0Kpu3F2/nXT/9icOfDuLXYCa+eBPEpcNkn0PvoUIenaVqQNFYiVg+5mRCMQILFU1qKp6AA\nd/dEKEBXHwHlrnJu+P4GrErx5PYthOV/CelXwOR7ISw61OFpmhZEjVUfVV/FnAzsVEpVichRwHDg\nTYwb47U5LvNGeGVde0ABHf46BaUU9/xyF5sKN/J83h56ORLgknnQt8lhuDVNa4f86ZI6D2Mozr7A\na0B/4O2ARhVA1XdHLerUA9BtCnN+f5SF2z7nn4X7mDDoXPjHLzohaFoH5k+Fulcp5RKRs4FnlVLP\niEib7X1UfY3C3viuQHHHrT5yV7Hiqxk8uusrjnEp/nrKK3pMZU3T/BuOU0TOBS4BzjSntdmStCpr\nO9auCexTxi50yOqj3FXkz/sbNzn20dMezQPnfYglLjnUUWma1gr4e0XzsRi3zt4iIr2BOYENK3Bc\n27NwpKRSVOHCbhXC7R3ods8eF/zwCK6Xj2W6rZhiRwRPnvYmsTohaJpmavJMQSm1RkSuBfqJyGHA\nJqXU/YEPLTCqsrKImjCBYqeLuAh77bEB2rNdGTDv75C7kqcHHM4yVx4PHHkPAzsPDHVkmqa1Iv6M\nvHY0sAl4BXgV2CAiEwIdWCB4Kypw796NIzWF4gpXx6g68rhh0ePw0jFQtIMvJt3C/1x5XDDwAqb0\nnRLq6DRNa2X8aVN4EjhFKZUBICKDgDeA9EAGFgiefcYtm6xdulBU7CKmvTcy78mEef+AHctg8Jls\nOfpa7vj2HwzvOpxbxt4S6ug0TWuF/EkKjuqEAKCUWicijgDGFDDK4wFAbHaKne722/PI64FfZ8G3\n/zZuTfGn1ygdcALXfXohEbYIHj/mcezWdrrvmqYdEn+SwnIReQHjgjWAi2mrN8RzuwEQm5WSChfJ\nnSKa+EAblL8JPr4Ksn+Hw06D055ERXXlzh9uIrskm5dPeJkeUT1CHaWmaa2UP0nh78C1QHV9wyLg\n2YBFFEA1ZwpWK0UVrvZ14ZrXC4tfhK/vAVsYnP0yDDsXRPjfmtf5avtX3DTmJsb2GBvqSDVNa8Ua\nTQoiMgzoC3yklHokOCEFjnIbSUFZrTW9j9qFvVvh43/C9p+h/4kw5WmI7QnAkrwlPLn8SSanTuay\nIZeFOFBN01q7xu6S+i+MEdaWA2NF5F6l1KtBiywQPEb1kRsLLo+77fc+8nph6Svw1V1gscIZz8HI\ni8DsZrurbBfTf5hOamwq9024r+N0v9U07aA11iX1YmC4UupcYCzwj+auXEROEpFMEdkkIjMbWe4c\nEVEiEtAeTdXVR+VGbmjbt80u3A5vnAELp0PKeLjqNxh1cU1CcHlc3PTDTTjdTp6a+BRRdj0WgqZp\nTWusVKxUSpUBKKX2iEizLv0VESvGiG2TgRxgiYjM9+3JZC4XA1wH/N6syA+CMhuay73G8BBtsvpI\nKVj+P/jiNkBgyjMw+tKaZFDt0aWPsnLPSh475jH6xPcJTayaprU5jSWFPj5jMwvQ13esZqXU2U2s\n+3CMq5+3AIjIXOAMIKPOcvcBDwM3Nyfwg2KeKZS5jKTQ5qqPinJg/jWw+Vvo/X9wxixjIJw6Fmxe\nwJz1c7hs8GWcmHZiCALVNK2taiwpnFPn/X+aue5EINvnfQ4wzncBERkNJCulPhWRBpOCiFwJXAmQ\nknJgIeiv6obmMo+ZFNrKmYJSsOIt+PxW8LrhlMeMQXAsB568Ze7N5N5f7yW9ezrXj7k+BMFqmtaW\nNTbIzjeB3LBZHfUEMK2pZZVSLwEvAaSnp6smFm94PWZDc5nZptAmqo+Kc2HBdbDxC0idYJwddO5d\n/6JVxdzw/Q3EOmJ59JhHsVnacJuJpmkhEchSYwfGqG3Vksxp1WKAocD3Zq+YHsB8ETldKbU0IBGZ\n1Uel7urqo1ZcaCoFq96Fz24GdxWc9DAcfmW9ZwcAlZ5K/rXoX+SW5vLaSa+RENGuRlHVNC1IAlkq\nLgH6m7fa3gFcAFxUPVMpVYTP+M8i8j0wPWAJgf3VR6Vmm0JMa21TKN0Nn9wA6z+B5HFGV9OEfg0u\nnrk3k5mLZrJp3yZuG3cbI7uNDGKwmqa1J34nBREJU0pV+ru8UsotIlcDXwBW4FWl1FoRuRdYqpSa\n3/xwD0119VGJSxFht+KwtcKxFNZ8AJ9Oh6oyOOHfMP4q4xqEeni8Hl5f+zr/WfEf4sPiee745zg6\n6eggB6xpWnvSZFIQkcMxbpsdB6SIyAjgL0qpa5r6rFJqIbCwzrQ7G1h2oj8BHxKPF4ASt2qd7Qmf\nToclL0PiGDjzBeg6oMFFs0uyuf2n21m+ezmTUydz5/g7iQ+PD2Kwmqa1R/6cKTwDnAbMA1BKrRSR\nNjmye/WZQqlLEd3a2hO2/2IkhLF/MdoPrPXHp5Tio00f8fDih7GKlQeOeoDT+pymr1bWNK1F+FMy\nWpRS2+sUOp4AxRNY1Vc0eyHSUX+VTEh4vfD5TIhNhMn3NZgQcktzeWDxA3yf/T3jeozj30f9W9/x\nVNO0FuVPUsg2q5CUeZXyNcCGwIYVGNUNzRVuCI9qRUlhxVuQuxLOeQUckQfMLneV89ra13htzWsI\nws3pNzN18FQszbvIXNM0rUn+JIV/YFQhpQC7gK85iPsgtQbV1UflXqOhuVVwFsM39xi9jIbWvl5Q\nKcVnWz/jiWVPsKt8Fyf3Ppkbx9yozw40TQuYJpOCUmo3RnfStq+6+sgDXVtLUlj0GJTtgYveqXX/\norX5a3lo8UOs2LOCwV0G8+gxjzKq26gQBqppWkfgT++jl4EDriJWSl0ZkIgCqLr6qNwDEa2hTWHv\nFvjteRhxkdHjCMivyOfp5U/z8aaP6RzemXuPvJcz+p2hq4o0TQsKf6qPvvZ5HQ6cRe17GrUd1dVH\nHghvDWcKX94BFjscfydVnireXPcmL658kSpvFdOGTuPKYVcS7YgOdZSapnUg/lQfveP7XkTeAH4K\nWEQBVHNDPBeE20N85L3lB1j/Ceq4O/hu3zoe+/oxskuyOTb5WKanTycl9uBv/KdpmnawDqazfm+g\ne0sHEgzVg+yUeULc0Oz1wue3srFzCo9UrOO37/5H37i+vDj5RY7sdWTo4tI0rcPzp02hkP1tChZg\nL9DgKGqtmll9VKkkpElhX8ZHzPLs5L24WKL2rufWw2/lvIHn6buaapoWco2WQmJcsTaC/Xc39Sql\nDvrW1aFWXX3kFUtIGprdXjfvZr7LrGUPUxYTzXkDz+OqkVfr21NomtZqNJoUlFJKRBYqpYYGK6BA\nUh63cetpkaA3NGeXZHPdd9exsXAj4yqczOh3Hv3H3x7UGDRN05riT2vrChFpHx3kPR6wGskgmNVH\nK/esZOrCqewu381T0cN4eU8R/Y+4MWjb1zRN81eDSUFEqs8iRgFLRCRTRJaLyB8isjw44bUs5fZJ\nCkGqPvpm+zdc8cUVRNmjePO45zh+3bfI8HMhqktQtq9pmtYcjVUfLQZGA6cHKZaAUx43yhybIBhd\nUjP3ZnLzjzczqPMgnj3+WTovexPcFTDu7wHftqZp2sFoLCkIgFJqc5BiCTyPF2WtTgqBPVOo9FQy\nc9FMYh2xRkKwx8LilyH1KOgxLKDb1jo2l8tFTk4OTqcz1KFoIRAeHk5SUhJ2+8GNGdNYUugqIg1W\nfCulnjioLYaQ75lCoNsUnlr2FJv2bWLW8bPoHN4Z1i2Aoiw48f6AblfTcnJyiImJIS0tTY+z0cEo\npSgoKCAnJ4fevXsf1Doaq0OxAtFATAOPtsfjQZkD3weyTeGXHb/w5ro3uWDgBfxf0v8ZE397AeJS\nYOApAduupgE4nU66dOmiE0IHJCJ06dLlkM4SGztTyFVK3XvQa26FlNuDN8BnCvuc+7j959vpE9eH\nG9PNE6281bD9J5h8b4MD6GhaS9IJoeM61L99k20K7Umgq4+UUtzz6z0UVhYy6/hZRNgijBm/vwD2\nSBh9aYtvU9M0rSU1Vn10fNCiCBa3B69ZfRQWgKQwb9M8vs76mmtGXcOgLoOMiWUFsOo9GH4+RHRq\n8W1qWmuzbds2hg5t+etdKyoqOOaYY/B49o8G/NRTTxEeHk5RUVHNtNdff52rr7661mcnTpzI0qVL\nASgtLeVvf/sbffv2ZcyYMUycOJHff//9kGJTSnHttdfSr18/hg8fzvLl9ffaf+eddxg+fDhDhgxh\nxowZB8z/4IMPEJGaWFevXs20adMOKbbmajApKKX2BjOQYFAeD57qNoUWTgrZxdk8uPhBxvYYy2WD\nL9s/Y9Hj4KmC8W1ysDpNazVeffVVzj77bKzW/f+7c+bMYezYsXz44Yd+r+cvf/kLnTt3ZuPGjSxb\ntozXXnuN/Pz8Q4rts88+Y+PGjWzcuJGXXnqJf/zjwP/3goICbr75Zr755hvWrl1LXl4e33zzTc38\nkpISnn76acaNG1czbdiwYeTk5JCVlXVI8TVHh6rgVh43XrFitQh2a8vVjrm9bmb+NBOb2Lh/wv1Y\nzSoqCjbD4pdg1FToOrDFtqdp/rpnwVoydha36DoH94rlrilDGl3G7XZz8cUXs3z5coYMGcLs2bOJ\njIwkLS2NpUuXkpCQwNKlS5k+fTrff/89P/zwA9dddx1g1In/+OOPxMTU7s/y1ltv8fbbb9e837x5\nM6WlpTz33HPcf//9XH755U3GvnnzZn7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tyGXtwbXc2/xe6nrUvbpj07tw4TiMiIZyLmfT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\no9FoLLRT0Gg0Go3F/wOr8DpALo2zqwAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "PCA данные\n",
            "Лучший параметр: {'max_depth': 14}\n",
            "score: 0.5622895622895623\n",
            "weighted f1 score: 0.5715806255362693\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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JQEhMDJbMscSfZ4SDooYPIyTCBxWQUrDtO/hxLmz5GldELB+NPoen7AXsryzgzD5ncs/Y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\nAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "nbgrader": {
          "grade": false,
          "grade_id": "cell-9eadd4d8a03ae67a",
          "locked": true,
          "schema_version": 2,
          "solution": false
        },
        "id": "anFxQE4b6OiD",
        "colab_type": "text"
      },
      "source": [
        "#### 2.6. Bagging.\n",
        "Перейдем к ансамблям. \n",
        "\n",
        "Для построения ансамбля воспользуемся Bagging'ом с числом алгоритмов от 2 до 50. В качестве первого семейства базовых алгоримтов будем использовать линейные модели (т.е. логистическую регрессию), в качестве второго - решающие деревья. \n",
        "\n",
        "*Пояснение: Будем строить ансамбль только из моделей из одного семейства, т.е. логистическая регрессия не смешивается с решающими деревьями.*\n",
        "\n",
        "Для этого можно сгенерировать с помощью метода bootstrap 50 подвыборок из `train` выборки (того же размера, что и исходная), обучить логистическую регрессию и решающее дерево с оптимальными параметрами из предыдущего пункта на каждой из подвыборок и затем усреднить предсказания k моделей.\n",
        "\n",
        "*Hint: в sklearn может найтись подходящая функция, которая облегчит вам реализацию данного пункта.*\n",
        "\n",
        "* Постройте график качества классификации и f1-score на `train` и `test` датасетах в зависимости от числа алгоритмов, вошедших в ансамбль.\n",
        "\n",
        "* Проанализируйте график. Какое количество моделей стоит использовать? Как вы думаете, являются ли параметры решающего дерева, подобранные в предыдущем пункте оптимальными в данном случае?"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "nbgrader": {
          "grade": true,
          "grade_id": "cell-8fc95a2b206bdae1",
          "locked": false,
          "points": 35,
          "schema_version": 2,
          "solution": true
        },
        "id": "a20L33J06OiE",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from sklearn.ensemble import BaggingClassifier"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BYSRRzCsaD3T",
        "colab_type": "code",
        "outputId": "4801e9b0-d092-4535-cbe8-1662e25c3c58",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 334
        }
      },
      "source": [
        "linear_score = []\n",
        "tree_score = []\n",
        "linear_f1 = []\n",
        "tree_f1 = []\n",
        "linear_max = (0, 0)\n",
        "tree_max = (0, 0)\n",
        "for i in range(2, 51):\n",
        "  clf_b = BaggingClassifier(LogisticRegression(C=100), n_estimators=i, bootstrap=True, bootstrap_features=False)\n",
        "  clf_b.fit(X_train_prep_pd, y_train)\n",
        "  linear_score.append(clf_b.score(X_test_prep_pd, y_test))\n",
        "  linear_f1.append(f1_score(y_test, clf_b.predict(X_test_prep_pd), average='weighted'))\n",
        "  if linear_max[1] < linear_score[-1]:\n",
        "    linear_max = (i, linear_score[-1])\n",
        "  clf_b = BaggingClassifier(DecisionTreeClassifier(max_depth=7), n_estimators=i, bootstrap=True, bootstrap_features=False)\n",
        "  clf_b.fit(X_train_prep_pd, y_train)\n",
        "  tree_score.append(clf_b.score(X_test_prep_pd, y_test))\n",
        "  tree_f1.append(f1_score(y_test, clf_b.predict(X_test_prep_pd), average='weighted'))\n",
        "  if tree_max[1] < tree_score[-1]:\n",
        "    tree_max = (i, tree_score[-1])\n",
        "  \n",
        "plt.plot(range(2, 51), linear_score, label='score of linear')\n",
        "plt.plot(range(2, 51), tree_score, label='score of decision tree')\n",
        "plt.plot(range(2, 51), linear_f1, label='f1 score of linear')\n",
        "plt.plot(range(2, 51), tree_f1, label='f1 score of decision tree')\n",
        "plt.xlabel('bootstrap size')\n",
        "plt.ylabel('Score')\n",
        "plt.legend(loc=\"lower right\")\n",
        "plt.show()\n",
        "print('Лучшее количество будстрапированных выборок.')\n",
        "print('Решающие деревья:', tree_max[0], '(score', tree_max[1], ').')\n",
        "print('Логистическая регрессия:', linear_max[0], '(score', linear_max[1], ').') "
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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6tiplQtebsObWxZrekfFdS37YqGiuDeqGTZfJlqh95Fhy+OrAF9hyGzJ16Ogq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\nABA5XqpIn/Pe3Z4AAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Лучшее количество будстрапированных выборок.\n",
            "Решающие деревья: 12 (score 0.7205387205387206 ).\n",
            "Логистическая регрессия: 10 (score 0.7811447811447811 ).\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SmWAoZV3hD6u",
        "colab_type": "code",
        "outputId": "9e91d922-fbe6-4246-8a51-ee89f8b993f1",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        }
      },
      "source": [
        "short_tree_score = []\n",
        "middle_tree_score = []\n",
        "deep_tree_score = []\n",
        "for i in range(2, 51):\n",
        "  clf_b = BaggingClassifier(DecisionTreeClassifier(max_depth=4), n_estimators=i, bootstrap=True, bootstrap_features=False)\n",
        "  clf_b.fit(X_train_prep_pd, y_train)\n",
        "  short_tree_score.append(clf_b.score(X_test_prep_pd, y_test))\n",
        "  \n",
        "  clf_b = BaggingClassifier(DecisionTreeClassifier(max_depth=7), n_estimators=i, bootstrap=True, bootstrap_features=False)\n",
        "  clf_b.fit(X_train_prep_pd, y_train)\n",
        "  middle_tree_score.append(clf_b.score(X_test_prep_pd, y_test))\n",
        "  \n",
        "  clf_b = BaggingClassifier(DecisionTreeClassifier(max_depth=20), n_estimators=i, bootstrap=True, bootstrap_features=False)\n",
        "  clf_b.fit(X_train_prep_pd, y_train)\n",
        "  deep_tree_score.append(clf_b.score(X_test_prep_pd, y_test))\n",
        "  \n",
        "plt.plot(range(2, 51), short_tree_score, label='score of short tree')\n",
        "plt.plot(range(2, 51), middle_tree_score, label='score of middle tree')\n",
        "plt.plot(range(2, 51), deep_tree_score, label='score of deep tree')\n",
        "plt.xlabel('bootstrap size')\n",
        "plt.ylabel('Score')\n",
        "plt.legend(loc=\"lower right\")\n",
        "plt.show()\n",
        "print('Из графика видно, что глубокие, переобученные деревья показывают в среднем более хороший результат на бэггинге.')"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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QUdnSObh4uO6iXlcvRblYZWOiB1VmXAm72U63s5s5xXqErW9QnsCkVoRXNzQ+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3PzzTej0+kwGo3cfffdmEwmHn30UW644Qb8fj8VFRX8+c9/5tOf/jTXX389K1aswGAwcP/992M2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\nYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Из графика видно, что глубокие, переобученные деревья показывают в среднем более хороший результат на бэггинге.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "sk2TwNF1jaUM",
        "colab_type": "text"
      },
      "source": [
        "Отдельно отметим, что в случае с логистической регрессией бэггинг не даёт существенный улучшений качества, на 1-2%. В случае же с деревьями прирост качества значительный, до 10%."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "nbgrader": {
          "grade": false,
          "grade_id": "cell-241b7691ab44cbfb",
          "locked": true,
          "schema_version": 2,
          "solution": false
        },
        "id": "BTLVGyx86OiH",
        "colab_type": "text"
      },
      "source": [
        "#### 2.7. Random Forest.\n",
        "Теперь воспользуйтесь `sklearn`-реализацией алгоритма Random Forest. \n",
        "\n",
        "* Постройте аналогичные графики для него (изменяя число деревьев от 1 до 50). Остальные параметры можно оставить по умолчанию.\n",
        "\n",
        "* Проанализируйте полученные результаты. Каким получилось оптимальное число деревьев. Как оно соотносится с оптимальным числом деревьев и линейных моделей в bagging'е из предыдущего пункта?"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "nbgrader": {
          "grade": true,
          "grade_id": "cell-888755d0f3d91620",
          "locked": false,
          "points": 15,
          "schema_version": 2,
          "solution": true
        },
        "id": "ub2An_ax6OiI",
        "colab_type": "code",
        "outputId": "2e3eeca9-af5e-482c-efae-5d24105d9a80",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        }
      },
      "source": [
        "from sklearn.ensemble import RandomForestClassifier\n",
        "max_iter = 50\n",
        "tree_score = []\n",
        "tree_f1 = []\n",
        "tree_max = (0, 0)\n",
        "for i in range(2, max_iter + 1):\n",
        "  clf_b = RandomForestClassifier(n_estimators=i)\n",
        "  clf_b.fit(X_train_prep_pd, y_train)\n",
        "  tree_score.append(clf_b.score(X_test_prep_pd, y_test))\n",
        "  tree_f1.append(f1_score(y_test, clf_b.predict(X_test_prep_pd), average='weighted'))\n",
        "  if tree_max[1] < tree_score[-1]:\n",
        "    tree_max = (i, tree_score[-1])\n",
        "  \n",
        "plt.plot(range(2, max_iter + 1), tree_score, label='score of Random Forest')\n",
        "plt.plot(range(2, max_iter + 1), tree_f1, label='f1 score of Random Forest')\n",
        "plt.xlabel('bootstrap size')\n",
        "plt.ylabel('Score')\n",
        "plt.legend(loc=\"lower right\")\n",
        "plt.show()\n",
        "print('Лучшее количество деревьев:', tree_max[0], '(score', tree_max[1], ').') "
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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F6TbvGJqQ+cCh6RO9/t+JjurmqkKqinL5+bEBLriDqhkvkdy+fMGvP4mmN0HV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\nYxpkwcIYY0yDLFgYY4xp0P8HOypFucycB34AAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Лучшее количество деревьев: 17 (score 0.7373737373737373 ).\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "85RxjLdToGRg",
        "colab_type": "text"
      },
      "source": [
        "Отметим, что случайный лес показывает себя лучше беггинга над деревьями. Уже при ~15-20 деревьях он выдаёт устойчиво хорошее качество. Конкретно на этих данных, лес и бэггинг над деревьями перестают улучшать результат примерно на одинаковом количестве бутстрапированных выборок.\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "nbgrader": {
          "grade": false,
          "grade_id": "cell-99191c0852538d4d",
          "locked": true,
          "schema_version": 2,
          "solution": false
        },
        "id": "mQPmjCty6OiL",
        "colab_type": "text"
      },
      "source": [
        "#### 2.8. Кривая обучения.\n",
        "* Поделите обучающую выборку на 10 примерно равных частей. Обучите логистическую регрессию (с оптимальными параметрами), решающее дерево (аналогично), бэггинг над логистическими регрессиями и решающими деревьями (с выбранными параметрами) и RandomForest (из предыдущего пункта) на одной, двух, трех и т.д. частях.\n",
        "\n",
        "* Постройти график f1-score и accuracy на `train` и `test` датасетах в зависимости от размера обучающей выборки.\n",
        "\n",
        "* Проанализируйте полученный график. Можно ли сделать из него какие-либо выводы? Если да - кратко опишите их."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "nbgrader": {
          "grade": true,
          "grade_id": "cell-e39bc7e7dff61ff9",
          "locked": false,
          "points": 15,
          "schema_version": 2,
          "solution": true
        },
        "id": "8tO6ngTh6OiM",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from sklearn.utils import shuffle\n",
        "\n",
        "X_train_prep_pd, y_train = shuffle(X_train_prep_pd, y_train, random_state=0)\n",
        "train_size = len(y_train)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WJ_U9D40KONA",
        "colab_type": "code",
        "outputId": "42efb33e-6bd6-4aa6-e82f-cc51de53ce50",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 549
        }
      },
      "source": [
        "logistic_score=[]\n",
        "tree_score=[]\n",
        "bagging_logistic_score=[]\n",
        "bagging_tree_score=[]\n",
        "random_forest_score=[]\n",
        "logistic_f1=[]\n",
        "tree_f1=[]\n",
        "bagging_logistic_f1=[]\n",
        "bagging_tree_f1=[]\n",
        "random_forest_f1=[]\n",
        "\n",
        "for i in range(1, 11):\n",
        "  n_features=round(train_size * i / 10)\n",
        "\n",
        "  clf = LogisticRegression(C=1000).fit(X_train_prep_pd[:n_features], y_train[:n_features])\n",
        "  logistic_score.append(clf.score(X_test_prep_pd, y_test))\n",
        "  logistic_f1.append(f1_score(y_test, clf.predict(X_test_prep_pd), average='weighted'))\n",
        "  \n",
        "  clf = DecisionTreeClassifier(max_depth=5).fit(X_train_prep_pd[:n_features], y_train[:n_features])\n",
        "  tree_score.append(clf.score(X_test_prep_pd, y_test))\n",
        "  tree_f1.append(f1_score(y_test, clf.predict(X_test_prep_pd), average='weighted'))\n",
        "  \n",
        "  clf = BaggingClassifier(LogisticRegression(C=1000), n_estimators=10).fit(X_train_prep_pd[:n_features], y_train[:n_features])\n",
        "  bagging_logistic_score.append(clf.score(X_test_prep_pd, y_test))\n",
        "  bagging_logistic_f1.append(f1_score(y_test, clf.predict(X_test_prep_pd), average='weighted'))\n",
        "  \n",
        "  clf = BaggingClassifier(DecisionTreeClassifier(max_depth=17), n_estimators=17).fit(X_train_prep_pd[:n_features], y_train[:n_features])\n",
        "  bagging_tree_score.append(clf.score(X_test_prep_pd, y_test))\n",
        "  bagging_tree_f1.append(f1_score(y_test, clf.predict(X_test_prep_pd), average='weighted'))\n",
        "  \n",
        "  clf = RandomForestClassifier(n_estimators=17).fit(X_train_prep_pd[:n_features], y_train[:n_features])\n",
        "  random_forest_score.append(clf.score(X_test_prep_pd, y_test))\n",
        "  random_forest_f1.append(f1_score(y_test, clf.predict(X_test_prep_pd), average='weighted'))\n",
        "\n",
        "plt.plot(range(1, 11), logistic_score, label='Логистическая регрессия')\n",
        "plt.plot(range(1, 11), tree_score, label='Решающее дерево')\n",
        "plt.plot(range(1, 11), bagging_logistic_score, label='Бэггинг над логистической регрессией')\n",
        "plt.plot(range(1, 11), bagging_tree_score, label='Бэггинг над решающими деревьями')\n",
        "plt.plot(range(1, 11), random_forest_score, label='Случайный лес')\n",
        "plt.xlabel('train sample size')\n",
        "plt.ylabel('Score')\n",
        "plt.legend(loc=\"lower right\")\n",
        "plt.show()\n",
        "plt.plot(range(1, 11), logistic_f1, label='Логистическая регрессия')\n",
        "plt.plot(range(1, 11), tree_f1, label='Решающее дерево')\n",
        "plt.plot(range(1, 11), bagging_logistic_f1, label='Бэггинг над логистической регрессией')\n",
        "plt.plot(range(1, 11), bagging_tree_f1, label='Бэггинг над решающими деревьями')\n",
        "plt.plot(range(1, 11), random_forest_f1, label='Случайный лес')\n",
        "plt.xlabel('train sample size')\n",
        "plt.ylabel('f1 score')\n",
        "plt.legend(loc=\"lower right\")\n",
        "plt.show()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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PD/HJrk+YljyNR2Y/osp+D3L2Xdw6xr491m7HvQ8SGbe9yzCO0+fs4Tx6pCcKvycK6Y3G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\nAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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KIFl6jPEMcO8AxnmK7UQf4K441/PUALeiPTgdbvatz2Pnymwa7S5Sx0cw8epk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\nnYVGo9FoSkU7C41Go9GUyv8DPUWwzgX08EMAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "mMLJbOSXQnlq",
        "colab_type": "text"
      },
      "source": [
        "Основной результат заключается в том, что с ростом размера обучающей выборки качество классификации в среднем ростёт. Однако отметим, что при увеличении размера выборки и преодолении отметки примерно в половину данных качесто классификации не сильно увеличивается. \n",
        "\n",
        "Сравним алгоритмы основанные на деревьях. Лучший результат, как и ожидалось, даёт случайный лес. Это происходит за счёт рандомизации по фичам и по элементам выборки. В Бэггинге над решающими деревьями есть рандомизация по выборкам, что даёт превосходство над обычным решающим деревом.\n",
        "\n",
        "Два алгоритма, основанные на логистической регрессии показывают примерно одинаковый результат. "
      ]
    }
  ]
}