homework_CometML.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "aUAqi67vLzhL"
      },
      "source": [
        "# Импорт библиотек"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 41,
      "metadata": {
        "id": "lXinfUuSL6Og"
      },
      "outputs": [],
      "source": [
        "import pandas as pd \n",
        "import seaborn as sns\n",
        "from matplotlib import pyplot as plt"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "hIlLTfPPz3VG"
      },
      "source": [
        "# Настройка CometML\n",
        "<img src='https://camo.githubusercontent.com/b35adf34bc440ea2ac19bb0d3432f48e26d71ab8e4c256e936b709eec44f5b19/68747470733a2f2f7777772e636f6d65742e6d6c2f696d616765732f6c6f676f5f636f6d65745f6c696768742e706e67' width=500px>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 42,
      "metadata": {
        "id": "4JiQtNAVQIu-"
      },
      "outputs": [],
      "source": [
        "#!pip install comet_ml"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 43,
      "metadata": {
        "id": "MGyR32T9qGD9"
      },
      "outputs": [],
      "source": [
        "from comet_ml import Experiment"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 44,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "LPU_kAoC9IYG",
        "outputId": "d2e6e899-e80a-455b-c18b-532ceee68a3d"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "COMET WARNING: As you are running in a Jupyter environment, you will need to call `experiment.end()` when finished to ensure all metrics and code are logged before exiting.\n",
            "COMET INFO: Couldn't find a Git repository in 'a:\\\\EDA' nor in any parent directory. You can override where Comet is looking for a Git Patch by setting the configuration `COMET_GIT_DIRECTORY`\n",
            "COMET INFO: Experiment is live on comet.ml https://www.comet.com/yyalexx/medical-appointment/2389125edcc64d7c94f3157ffec7754f\n",
            "\n"
          ]
        }
      ],
      "source": [
        "# Создайте эксперимент с помощью вашего API ключа\n",
        "experiment = Experiment(\n",
        "    api_key='H1XoY5Q5aXov539DDOWrHbmQY',\n",
        "    project_name='medical-appointment',\n",
        "    workspace='yyalexx',\n",
        ")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "y3290WaEMCCM"
      },
      "source": [
        "# Загрузка данных"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 45,
      "metadata": {
        "id": "VbAf186tLgqb"
      },
      "outputs": [],
      "source": [
        "df = pd.read_csv('./KaggleV2-May-2016.csv')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 46,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "Yrs_ikOi7e92",
        "outputId": "7adfe956-3f2b-48eb-d83f-b6bfc2a9a07a"
      },
      "outputs": [
        {
          "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>PatientId</th>\n",
              "      <th>AppointmentID</th>\n",
              "      <th>Gender</th>\n",
              "      <th>ScheduledDay</th>\n",
              "      <th>AppointmentDay</th>\n",
              "      <th>Age</th>\n",
              "      <th>Neighbourhood</th>\n",
              "      <th>Scholarship</th>\n",
              "      <th>Hipertension</th>\n",
              "      <th>Diabetes</th>\n",
              "      <th>Alcoholism</th>\n",
              "      <th>Handcap</th>\n",
              "      <th>SMS_received</th>\n",
              "      <th>No-show</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2.987250e+13</td>\n",
              "      <td>5642903</td>\n",
              "      <td>F</td>\n",
              "      <td>2016-04-29T18:38:08Z</td>\n",
              "      <td>2016-04-29T00:00:00Z</td>\n",
              "      <td>62</td>\n",
              "      <td>JARDIM DA PENHA</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>No</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>5.589978e+14</td>\n",
              "      <td>5642503</td>\n",
              "      <td>M</td>\n",
              "      <td>2016-04-29T16:08:27Z</td>\n",
              "      <td>2016-04-29T00:00:00Z</td>\n",
              "      <td>56</td>\n",
              "      <td>JARDIM DA PENHA</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>No</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>4.262962e+12</td>\n",
              "      <td>5642549</td>\n",
              "      <td>F</td>\n",
              "      <td>2016-04-29T16:19:04Z</td>\n",
              "      <td>2016-04-29T00:00:00Z</td>\n",
              "      <td>62</td>\n",
              "      <td>MATA DA PRAIA</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>No</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>8.679512e+11</td>\n",
              "      <td>5642828</td>\n",
              "      <td>F</td>\n",
              "      <td>2016-04-29T17:29:31Z</td>\n",
              "      <td>2016-04-29T00:00:00Z</td>\n",
              "      <td>8</td>\n",
              "      <td>PONTAL DE CAMBURI</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>No</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>8.841186e+12</td>\n",
              "      <td>5642494</td>\n",
              "      <td>F</td>\n",
              "      <td>2016-04-29T16:07:23Z</td>\n",
              "      <td>2016-04-29T00:00:00Z</td>\n",
              "      <td>56</td>\n",
              "      <td>JARDIM DA PENHA</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>No</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "      PatientId  AppointmentID Gender          ScheduledDay  \\\n",
              "0  2.987250e+13        5642903      F  2016-04-29T18:38:08Z   \n",
              "1  5.589978e+14        5642503      M  2016-04-29T16:08:27Z   \n",
              "2  4.262962e+12        5642549      F  2016-04-29T16:19:04Z   \n",
              "3  8.679512e+11        5642828      F  2016-04-29T17:29:31Z   \n",
              "4  8.841186e+12        5642494      F  2016-04-29T16:07:23Z   \n",
              "\n",
              "         AppointmentDay  Age      Neighbourhood  Scholarship  Hipertension  \\\n",
              "0  2016-04-29T00:00:00Z   62    JARDIM DA PENHA            0             1   \n",
              "1  2016-04-29T00:00:00Z   56    JARDIM DA PENHA            0             0   \n",
              "2  2016-04-29T00:00:00Z   62      MATA DA PRAIA            0             0   \n",
              "3  2016-04-29T00:00:00Z    8  PONTAL DE CAMBURI            0             0   \n",
              "4  2016-04-29T00:00:00Z   56    JARDIM DA PENHA            0             1   \n",
              "\n",
              "   Diabetes  Alcoholism  Handcap  SMS_received No-show  \n",
              "0         0           0        0             0      No  \n",
              "1         0           0        0             0      No  \n",
              "2         0           0        0             0      No  \n",
              "3         0           0        0             0      No  \n",
              "4         1           0        0             0      No  "
            ]
          },
          "execution_count": 46,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df.head()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "OzCY1MkvTJSR"
      },
      "source": [
        "# Визуализация"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ptpoultBWCj_"
      },
      "source": [
        "### Распределение числовых признаков"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 47,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 877
        },
        "id": "h6WZ8PnRSUx0",
        "outputId": "a1138b19-a153-49f6-c1d9-299756897d32"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "{'web': 'https://www.comet.com/api/image/download?imageId=54c49fa35a54489d974183f597f506e0&experimentKey=2389125edcc64d7c94f3157ffec7754f',\n",
              " 'api': 'https://www.comet.com/api/rest/v1/image/get-image?imageId=54c49fa35a54489d974183f597f506e0&experimentKey=2389125edcc64d7c94f3157ffec7754f',\n",
              " 'imageId': '54c49fa35a54489d974183f597f506e0'}"
            ]
          },
          "execution_count": 47,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 1152x1008 with 9 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "df.hist(figsize=(16,14));\n",
        "#логируем гистограмму\n",
        "experiment.log_figure(figure=plt)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5BNTQ-1tWG2r"
      },
      "source": [
        "*Проанализируем число людей каждого возраста*"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 48,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "RGNyu_sJTYfS",
        "outputId": "3df9516a-eee3-46a4-c8f3-82d26ebd6634"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Уникальные значения в `Age` => [ 62  56   8  76  23  39  21  19  30  29  22  28  54  15  50  40  46   4\n",
            "  13  65  45  51  32  12  61  38  79  18  63  64  85  59  55  71  49  78\n",
            "  31  58  27   6   2  11   7   0   3   1  69  68  60  67  36  10  35  20\n",
            "  26  34  33  16  42   5  47  17  41  44  37  24  66  77  81  70  53  75\n",
            "  73  52  74  43  89  57  14   9  48  83  72  25  80  87  88  84  82  90\n",
            "  94  86  91  98  92  96  93  95  97 102 115 100  99  -1]\n"
          ]
        }
      ],
      "source": [
        "print(\"Уникальные значения в `Age` => {}\".format(df.Age.unique()))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 49,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "eb1dWovPWYjz",
        "outputId": "85c41a9d-c7fc-4114-bbbc-9e0c861d272e"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "0      3539\n",
              "1      2273\n",
              "52     1746\n",
              "49     1652\n",
              "53     1651\n",
              "       ... \n",
              "97       11\n",
              "98        6\n",
              "100       4\n",
              "102       2\n",
              "99        1\n",
              "Name: Age, Length: 102, dtype: int64"
            ]
          },
          "execution_count": 49,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# Удалим аномальные значения\n",
        "df = df[(df.Age >= 0) & (df.Age <= 110)]\n",
        "df.Age.value_counts()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 50,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 413
        },
        "id": "OMFj0V8NXMfe",
        "outputId": "0feefc02-6433-48f7-e647-4b63eebb0cd1"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 1728x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "plt.figure(figsize=(24,6))\n",
        "plt.xticks(rotation=90)\n",
        "ax = sns.countplot(x=df.Age)\n",
        "ax.set_title(\"Распределение пациентов по возрасту\")\n",
        "experiment.log_figure(figure=plt)\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "kq473GcDZFek"
      },
      "source": [
        "# Придет ли пациент на назначенный приием? "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 51,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5oXcfkNKZjdd",
        "outputId": "ff0c57dc-7ef6-43b4-82bc-958ee2bacfcc"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "Int64Index: 110521 entries, 0 to 110526\n",
            "Data columns (total 14 columns):\n",
            " #   Column          Non-Null Count   Dtype  \n",
            "---  ------          --------------   -----  \n",
            " 0   PatientId       110521 non-null  float64\n",
            " 1   AppointmentID   110521 non-null  int64  \n",
            " 2   Gender          110521 non-null  object \n",
            " 3   ScheduledDay    110521 non-null  object \n",
            " 4   AppointmentDay  110521 non-null  object \n",
            " 5   Age             110521 non-null  int64  \n",
            " 6   Neighbourhood   110521 non-null  object \n",
            " 7   Scholarship     110521 non-null  int64  \n",
            " 8   Hipertension    110521 non-null  int64  \n",
            " 9   Diabetes        110521 non-null  int64  \n",
            " 10  Alcoholism      110521 non-null  int64  \n",
            " 11  Handcap         110521 non-null  int64  \n",
            " 12  SMS_received    110521 non-null  int64  \n",
            " 13  No-show         110521 non-null  object \n",
            "dtypes: float64(1), int64(8), object(5)\n",
            "memory usage: 12.6+ MB\n"
          ]
        }
      ],
      "source": [
        "df.info()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "24uKsoaBjH0B"
      },
      "source": [
        "## Кодировка категориальных признаков"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 52,
      "metadata": {
        "id": "1J8JAzwmjeKW"
      },
      "outputs": [],
      "source": [
        "categorical_columns_names = ['Gender', 'Neighbourhood']"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 53,
      "metadata": {
        "id": "lgOtCCRTkIle"
      },
      "outputs": [],
      "source": [
        "encoded_columns = pd.get_dummies(df, columns = categorical_columns_names)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 54,
      "metadata": {
        "id": "okMGD3TmmGpE"
      },
      "outputs": [],
      "source": [
        "encoded_columns.drop(['AppointmentID', 'PatientId', 'ScheduledDay', 'AppointmentDay', 'No-show'], axis=1, inplace = True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 55,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 317
        },
        "id": "fNGfrwh7mtR3",
        "outputId": "9da1b29a-394e-4f76-baa9-47054f2a3b9a"
      },
      "outputs": [
        {
          "data": {
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              "        vertical-align: middle;\n",
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              "\n",
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              "    }\n",
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              "  <thead>\n",
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              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>56</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5 rows × 90 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "   Age  Scholarship  Hipertension  Diabetes  Alcoholism  Handcap  \\\n",
              "0   62            0             1         0           0        0   \n",
              "1   56            0             0         0           0        0   \n",
              "2   62            0             0         0           0        0   \n",
              "3    8            0             0         0           0        0   \n",
              "4   56            0             1         1           0        0   \n",
              "\n",
              "   SMS_received  Gender_F  Gender_M  Neighbourhood_AEROPORTO  ...  \\\n",
              "0             0         1         0                        0  ...   \n",
              "1             0         0         1                        0  ...   \n",
              "2             0         1         0                        0  ...   \n",
              "3             0         1         0                        0  ...   \n",
              "4             0         1         0                        0  ...   \n",
              "\n",
              "   Neighbourhood_SANTOS REIS  Neighbourhood_SEGURANÇA DO LAR  \\\n",
              "0                          0                               0   \n",
              "1                          0                               0   \n",
              "2                          0                               0   \n",
              "3                          0                               0   \n",
              "4                          0                               0   \n",
              "\n",
              "   Neighbourhood_SOLON BORGES  Neighbourhood_SÃO BENEDITO  \\\n",
              "0                           0                           0   \n",
              "1                           0                           0   \n",
              "2                           0                           0   \n",
              "3                           0                           0   \n",
              "4                           0                           0   \n",
              "\n",
              "   Neighbourhood_SÃO CRISTÓVÃO  Neighbourhood_SÃO JOSÉ  \\\n",
              "0                            0                       0   \n",
              "1                            0                       0   \n",
              "2                            0                       0   \n",
              "3                            0                       0   \n",
              "4                            0                       0   \n",
              "\n",
              "   Neighbourhood_SÃO PEDRO  Neighbourhood_TABUAZEIRO  \\\n",
              "0                        0                         0   \n",
              "1                        0                         0   \n",
              "2                        0                         0   \n",
              "3                        0                         0   \n",
              "4                        0                         0   \n",
              "\n",
              "   Neighbourhood_UNIVERSITÁRIO  Neighbourhood_VILA RUBIM  \n",
              "0                            0                         0  \n",
              "1                            0                         0  \n",
              "2                            0                         0  \n",
              "3                            0                         0  \n",
              "4                            0                         0  \n",
              "\n",
              "[5 rows x 90 columns]"
            ]
          },
          "execution_count": 55,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "encoded_columns.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 56,
      "metadata": {
        "id": "aqllZjq0Z0Rp"
      },
      "outputs": [],
      "source": [
        "y = df['No-show']"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 57,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "q84BeK5ttWRe",
        "outputId": "b1752a6d-6ab9-48e2-d77d-e5ccc95baaff"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "0         No\n",
              "1         No\n",
              "2         No\n",
              "3         No\n",
              "4         No\n",
              "          ..\n",
              "110522    No\n",
              "110523    No\n",
              "110524    No\n",
              "110525    No\n",
              "110526    No\n",
              "Name: No-show, Length: 110521, dtype: object"
            ]
          },
          "execution_count": 57,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "y"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 58,
      "metadata": {
        "id": "H5wB-62WnxUY"
      },
      "outputs": [],
      "source": [
        "y = y.replace({'No': 0, 'Yes': 1})"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 59,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "oX2skGG-odCx",
        "outputId": "a4f32ca5-3fcd-482e-842f-c85daa739eec"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "0    0.798084\n",
              "1    0.201916\n",
              "Name: No-show, dtype: float64"
            ]
          },
          "execution_count": 59,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "y.value_counts(True)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "HFqVvPJAnKmt"
      },
      "source": [
        "## Шкалирование данных"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 60,
      "metadata": {
        "id": "iY4UeiB1nN0f"
      },
      "outputs": [],
      "source": [
        "from sklearn.preprocessing import MinMaxScaler"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 61,
      "metadata": {
        "id": "jOY0PfPWnVV4"
      },
      "outputs": [],
      "source": [
        "scaler = MinMaxScaler()\n",
        "X = scaler.fit_transform(encoded_columns)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 62,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "I5OtnT9gm8LP",
        "outputId": "17283f66-be6c-4f47-f691-c3c67c12e1d6"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "(110521, 90)\n"
          ]
        }
      ],
      "source": [
        "print(X.shape)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Qo2K5d25o8bF"
      },
      "source": [
        "# Модель (этот раздел будет подробно изучен далее в курсе)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 63,
      "metadata": {
        "id": "nZDLlWa4Z2k4"
      },
      "outputs": [],
      "source": [
        "from sklearn.linear_model import LogisticRegression\n",
        "from sklearn.metrics import accuracy_score"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "XebBCLaaaN-o"
      },
      "source": [
        "### Инициализация модели"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 64,
      "metadata": {
        "id": "LQqudwBtaCRs"
      },
      "outputs": [],
      "source": [
        "logreg = LogisticRegression()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 65,
      "metadata": {
        "id": "I6YmYYobaT-u"
      },
      "outputs": [],
      "source": [
        "random_state = 42"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "A_gkA53ybxqa"
      },
      "source": [
        "### Обучение модели"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 66,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "R1BSRYG9amr7",
        "outputId": "7b6876f8-40b4-4896-9069-5eeb20c87e96"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "c:\\Users\\Rui\\AppData\\Local\\Programs\\Python\\Python39\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
            "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
            "\n",
            "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
            "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
            "Please also refer to the documentation for alternative solver options:\n",
            "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
            "  n_iter_i = _check_optimize_result(\n"
          ]
        },
        {
          "data": {
            "text/html": [
              "<style>#sk-container-id-3 {color: black;background-color: white;}#sk-container-id-3 pre{padding: 0;}#sk-container-id-3 div.sk-toggleable {background-color: white;}#sk-container-id-3 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-3 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-3 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-3 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-3 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-3 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-3 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-3 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-3 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-3 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-3 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-3 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-3 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-3 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-3 div.sk-item {position: relative;z-index: 1;}#sk-container-id-3 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-3 div.sk-item::before, #sk-container-id-3 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-3 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-3 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-3 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-3 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-3 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-3 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-3 div.sk-label-container {text-align: center;}#sk-container-id-3 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-3 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-3\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LogisticRegression()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-3\" type=\"checkbox\" checked><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LogisticRegression</label><div class=\"sk-toggleable__content\"><pre>LogisticRegression()</pre></div></div></div></div></div>"
            ],
            "text/plain": [
              "LogisticRegression()"
            ]
          },
          "execution_count": 66,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "logreg.fit(X, y)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6YN1DZFicf4l"
      },
      "source": [
        "#### Логирование параметров обучения"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 67,
      "metadata": {
        "id": "wGADA8ercl7k"
      },
      "outputs": [],
      "source": [
        "params={\"random_state\":random_state,\n",
        "        \"model_type\":\"logreg\"\n",
        "}"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 68,
      "metadata": {
        "id": "ZOwq-iBJcsKU"
      },
      "outputs": [],
      "source": [
        "experiment.log_parameters(params)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "l4gGfkqtb4IR"
      },
      "source": [
        "### Предсказания модели"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 69,
      "metadata": {
        "id": "Z-_AH_f3b55w"
      },
      "outputs": [],
      "source": [
        "y_pred = logreg.predict(X)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "X5o9LBBab-cH"
      },
      "source": [
        "### Анализ модели"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 70,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "uigAO6s1Z-xK",
        "outputId": "7c10a88f-14ad-48b4-b1b5-2a4ae23c46d3"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Доля правильных ответов:  0.798\n"
          ]
        }
      ],
      "source": [
        "accuracy = accuracy_score(y, y_pred)\n",
        "\n",
        "print(\"Доля правильных ответов: {:6.3f}\".format(accuracy))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "nltneNyYdAcE"
      },
      "source": [
        "# Логирование метрик"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 71,
      "metadata": {
        "id": "46QiPA6jc0gQ"
      },
      "outputs": [],
      "source": [
        "metrics = {\"accuracy\":accuracy}"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 72,
      "metadata": {
        "id": "nr_zJEO_c5TY"
      },
      "outputs": [],
      "source": [
        "experiment.log_metrics(metrics)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9U26OZqibj3P"
      },
      "source": [
        "<img src = 'https://miro.medium.com/max/1400/0*r2yYhe2DpE4d3WKP.png' width=700px>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 73,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "uSvXz5ESf6cf",
        "outputId": "5ad9a064-c2dd-497d-b6df-64f610e7cd63"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "{'web': 'https://www.comet.com/api/asset/download?assetId=478a5851ea1b465ea23721d818a0add0&experimentKey=2389125edcc64d7c94f3157ffec7754f',\n",
              " 'api': 'https://www.comet.com/api/rest/v2/experiment/asset/get-asset?assetId=478a5851ea1b465ea23721d818a0add0&experimentKey=2389125edcc64d7c94f3157ffec7754f',\n",
              " 'assetId': '478a5851ea1b465ea23721d818a0add0'}"
            ]
          },
          "execution_count": 73,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "experiment.log_confusion_matrix(y.tolist(), y_pred.tolist())"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5ydNwtkThhPg"
      },
      "source": [
        "# Домашнее задание"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Vit5v66shjwQ"
      },
      "source": [
        "Визуализируйте корреляцию между числовыми признаками, использованными для обучения модели и залогируйте изображение в Comet.\n",
        "\n",
        "В качестве ответа приложите обновленный ноутбук.\n",
        "\n",
        "*Форма оценки ментором:*\n",
        "\n",
        "1) Проведена визуализация корреляции между 7 числовыми признаками, использованными для обучения модели - 5 баллов\n",
        "\n",
        "2) Произведено логирование графика в CometMl - 10 баллов\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 74,
      "metadata": {},
      "outputs": [],
      "source": [
        "df_nmb = df.drop(['Gender', 'Neighbourhood', 'AppointmentID', 'PatientId', 'ScheduledDay', \n",
        "                  'AppointmentDay', 'No-show'], axis=1, inplace = False)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 75,
      "metadata": {},
      "outputs": [
        {
          "data": {
            "text/plain": [
              "<seaborn.axisgrid.PairGrid at 0x1dfa2179880>"
            ]
          },
          "execution_count": 75,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "data": {
            "image/png": 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            "text/plain": [
              "<Figure size 1260x1260 with 56 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "sns.pairplot(df_nmb)\n",
        "# Загрузить на comet не получилось из-за превышения размера."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "На главной диагонали представлены графики-распределения конкретной величины. \n",
        "Признаки не распределены нормально. Поэтому применим ранговую корреляцию для числовых переменных, которые распределены ненормально или если между ними существует нелинейная связь. Используем корреляцию Спирмена."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 76,
      "metadata": {},
      "outputs": [
        {
          "data": {
            "text/plain": [
              "{'web': 'https://www.comet.com/api/image/download?imageId=0418c3c35f3f47a89f17ad16486ab795&experimentKey=2389125edcc64d7c94f3157ffec7754f',\n",
              " 'api': 'https://www.comet.com/api/rest/v1/image/get-image?imageId=0418c3c35f3f47a89f17ad16486ab795&experimentKey=2389125edcc64d7c94f3157ffec7754f',\n",
              " 'imageId': '0418c3c35f3f47a89f17ad16486ab795'}"
            ]
          },
          "execution_count": 76,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 864x720 with 2 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "# Создадим холст и оси\n",
        "fig, ax = plt.subplots(figsize=(12, 10))\n",
        "# Построим тепловую карту\n",
        "heatmap = sns.heatmap(df_nmb.corr(method = 'spearman'), annot=True, cmap=\"YlGnBu\")\n",
        "heatmap.set_title('Корреляция между 7 числовыми признаками, использованными для обучения модели')\n",
        "experiment.log_figure(heatmap)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 77,
      "metadata": {},
      "outputs": [
        {
          "data": {
            "text/html": [
              "\n",
              "        <iframe\n",
              "            width=\"100%\"\n",
              "            height=\"800px\"\n",
              "            src=\"https://www.comet.com/yyalexx/medical-appointment/2389125edcc64d7c94f3157ffec7754f\"\n",
              "            frameborder=\"0\"\n",
              "            allowfullscreen\n",
              "            \n",
              "        ></iframe>\n",
              "        "
            ],
            "text/plain": [
              "<IPython.lib.display.IFrame at 0x1dfa0002fd0>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "experiment.display()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 78,
      "metadata": {},
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "COMET INFO: ---------------------------\n",
            "COMET INFO: Comet.ml Experiment Summary\n",
            "COMET INFO: ---------------------------\n",
            "COMET INFO:   Data:\n",
            "COMET INFO:     display_summary_level : 1\n",
            "COMET INFO:     url                   : https://www.comet.com/yyalexx/medical-appointment/2389125edcc64d7c94f3157ffec7754f\n",
            "COMET INFO:   Metrics:\n",
            "COMET INFO:     accuracy : 0.7980836221170637\n",
            "COMET INFO:   Parameters:\n",
            "COMET INFO:     C                 : 1.0\n",
            "COMET INFO:     class_weight      : 1\n",
            "COMET INFO:     clip              : False\n",
            "COMET INFO:     copy              : True\n",
            "COMET INFO:     dual              : False\n",
            "COMET INFO:     feature_range     : (0, 1)\n",
            "COMET INFO:     fit_intercept     : True\n",
            "COMET INFO:     intercept_scaling : 1\n",
            "COMET INFO:     l1_ratio          : 1\n",
            "COMET INFO:     max_iter          : 100\n",
            "COMET INFO:     model_type        : logreg\n",
            "COMET INFO:     multi_class       : auto\n",
            "COMET INFO:     n_jobs            : 1\n",
            "COMET INFO:     penalty           : l2\n",
            "COMET INFO:     random_state      : 42\n",
            "COMET INFO:     solver            : lbfgs\n",
            "COMET INFO:     tol               : 0.0001\n",
            "COMET INFO:     verbose           : 0\n",
            "COMET INFO:     warm_start        : False\n",
            "COMET INFO:   Uploads:\n",
            "COMET INFO:     confusion-matrix    : 1\n",
            "COMET INFO:     environment details : 1\n",
            "COMET INFO:     figures             : 3\n",
            "COMET INFO:     filename            : 1\n",
            "COMET INFO:     installed packages  : 1\n",
            "COMET INFO:     notebook            : 1\n",
            "COMET INFO:     source_code         : 1\n",
            "COMET INFO: ---------------------------\n",
            "COMET INFO: Uploading 1 metrics, params and output messages\n",
            "COMET INFO: Waiting for completion of the file uploads (may take several seconds)\n",
            "COMET INFO: The Python SDK has 10800 seconds to finish before aborting...\n",
            "COMET INFO: Still uploading 3 file(s), remaining 45.29 KB/328.57 KB\n"
          ]
        }
      ],
      "source": [
        "experiment.end()"
      ]
    }
  ],
  "metadata": {
    "colab": {
      "collapsed_sections": [],
      "name": "noshowappointments_cometml.ipynb",
      "provenance": []
    },
    "kernelspec": {
      "display_name": "Python 3.9.12 64-bit",
      "language": "python",
      "name": "python3"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.9.12"
    },
    "vscode": {
      "interpreter": {
        "hash": "93e5499867a482d1f69126f65754e04e21327fbe16c28c259dff18a59fe0a8c0"
      }
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}