Split System 1.ipynb

{
  "cells": [
    {
      "cell_type": "code",
      "execution_count": 1,
      "id": "de6ffe38-c190-4ebc-85bd-99d59e672a10",
      "metadata": {
        "id": "de6ffe38-c190-4ebc-85bd-99d59e672a10"
      },
      "outputs": [],
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "from scipy import stats"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "3448a602",
      "metadata": {
        "id": "3448a602"
      },
      "source": [
        "# Условие"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "0f7fb6fe-cb90-45e6-b170-5bcf1baa3e09",
      "metadata": {
        "id": "0f7fb6fe-cb90-45e6-b170-5bcf1baa3e09"
      },
      "source": [
        "**В этом домашнем задании вы должны написать свою сплит систему для проведения A/B тестов**\n",
        "\n",
        "В предоставленных данных есть 50000 юзеров, для каждого есть запись какой-то метрики за 2 года (730 дней).\n",
        "\n",
        "Данные: https://disk.yandex.ru/d/2mH8fuKCKLeozg\n",
        "\n",
        "\n",
        "### Ваша задача:\n",
        "\n",
        "1) Написать алгоритм, которые разбивает данные на 3 группы размера около 5000, которые будут считаться \"похожими\".\n",
        "\n",
        "- Для этого вам самостоятельно нужно решить, какие критерии схожести тут применимы и построить необходимые тесты для выбора этих групп.\n",
        "\n",
        "- Данные 3 группы должны считаться похожими на основе ваших метрик.\n",
        "\n",
        "- Разбиение затем будет протестировано на следующих 2х годах нами.\n",
        "\n",
        "2) Визуализировать распределения выбранных вами метрик для найденных групп и сравнить с аналогичными разультатами для случайных групп"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "b7e83afd",
      "metadata": {
        "id": "b7e83afd"
      },
      "source": [
        "### Нужно реализовать следующие функции (вы можете добавлять какие-то побочные, если считаете нужным):\n",
        "\n",
        "`generate_groups`  - функция, которая по данным и числу групп делает случайное разбиение данных на примерно одинаковые части по количеству групп\n",
        "\n",
        "`conduct_test_on_pair` - функция проводит статистические тесты между двумя данными группами и возвращает нужные статистики и результаты\n",
        "\n",
        "`conduct_tests` -- по списку групп и количеству необходимых похожих групп (зафиксировано в условии задачи как 3) перебирает возможные тройки групп пока не найдет похожие, критерий остановки за вами\n",
        "\n",
        "`find_groups` -- берет данные и число групп, на которое вы их разбиваете и ищет 3 группы удовлетворяющие вашим критериям схожести.\n",
        "\n",
        "Рекомендуем начинать с того, чтобы искать разбиение на 2 группы, а не 3, чтобы проверить, что сплит система работает адекватно. Задача с двумя группами зачастую сильно проще, чем с тремя."
      ]
    },
    {
      "cell_type": "markdown",
      "id": "d6e5a5aa-c928-4df4-b5ae-1991c73e2546",
      "metadata": {
        "id": "d6e5a5aa-c928-4df4-b5ae-1991c73e2546"
      },
      "source": [
        "Группы должны состоять из уникальных клиентов без дубликатов, один клиент может быть только в одной группе. То есть должны выбирать группы из датасета без повторений и у групп не должно быть пересечений."
      ]
    },
    {
      "cell_type": "markdown",
      "id": "7faa80d2-68b8-4cb4-b1f2-a9cb63f9a8f0",
      "metadata": {
        "id": "7faa80d2-68b8-4cb4-b1f2-a9cb63f9a8f0"
      },
      "source": [
        " Про то, по каким параметрам оценивать группы: в этом задании вам требуется найти группы, в которых распределения **средних значений и дисперсии данной последовательности** по пользователям похожи. То есть вам нужно вычислить среднее значение и дисперсию данной последовательности для каждого пользователя, а затем проверить, что распределения результатов между группами схожи по вашим тестам.\n",
        "\n",
        "\n",
        "**Замечание**: Для разбиения на группы и сравнения похожести можете брать любую метрику и считать любой критерии, но мы будем сравнивать именно метрику «среднее» и дисперсию на следующих по времени данных"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "a079967d-5c65-44a2-8e93-ed954459e5a7",
      "metadata": {
        "tags": [],
        "id": "a079967d-5c65-44a2-8e93-ed954459e5a7"
      },
      "source": [
        "#### Про систему оценивания: При оценивании мы будем проверять полученные вами группы как на этих данных, так и на следующих двух годах. При проверке будем учитывать и качество полученных групп при помощи метрик."
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import gdown\n",
        "from scipy.stats import mannwhitneyu, ttest_ind, levene\n",
        "from itertools import combinations\n",
        "import matplotlib\n",
        "import seaborn as sns\n",
        "\n",
        "# Загрузим файл по ссылке с Google Диска\n",
        "url = 'https://drive.google.com/uc?id=1qiDcTapez7wmLTuweRfX9O9Y4kRYEmfs'\n",
        "data = 'train_data.csv'\n",
        "gdown.download(url, data, quiet=False)\n",
        "\n",
        "df = pd.read_csv(data, delimiter=',')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "h0cVKnvCC2S3",
        "outputId": "5d7271f4-6830-4846-ef9c-883bee4b582e"
      },
      "id": "h0cVKnvCC2S3",
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Downloading...\n",
            "From (original): https://drive.google.com/uc?id=1qiDcTapez7wmLTuweRfX9O9Y4kRYEmfs\n",
            "From (redirected): https://drive.google.com/uc?id=1qiDcTapez7wmLTuweRfX9O9Y4kRYEmfs&confirm=t&uuid=a6b2c09e-395e-4595-b21f-ff6ff1015626\n",
            "To: /content/train_data.csv\n",
            "100%|██████████| 692M/692M [00:06<00:00, 110MB/s]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.head() # Посомтри, как выглядят наши данные"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 273
        },
        "id": "ZflzGH21FoWQ",
        "outputId": "584377ea-1add-4935-e208-5e79c676e4a7"
      },
      "id": "ZflzGH21FoWQ",
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   2019-01-01  2019-01-02  2019-01-03  2019-01-04  2019-01-05  2019-01-06  \\\n",
              "0   -3.533155   -4.475755   -4.748074   -5.569953   -8.017896  -12.396771   \n",
              "1   -3.824475   -1.530769    0.038338   -1.475237   -1.602029   -0.992509   \n",
              "2   -1.616434   -4.658834   -6.746498   -9.979424  -11.683396   -9.507703   \n",
              "3   -0.157282    1.827617   -2.585284   -4.397189    0.113494    7.614615   \n",
              "4    2.056969   10.187929    8.559520    8.579555    9.464262    9.038088   \n",
              "\n",
              "   2019-01-07  2019-01-08  2019-01-09  2019-01-10  ...  2020-12-21  \\\n",
              "0  -11.853961  -12.046498  -10.297042  -10.075553  ... -168.163849   \n",
              "1    0.302906    1.789140    4.177589    9.273372  ...   75.919063   \n",
              "2   -7.614438   -9.735666  -10.754863  -11.058539  ...   47.759469   \n",
              "3    5.456654    5.928451    6.953534    4.514409  ...  318.364962   \n",
              "4   10.875990    9.883777    8.026472   13.040411  ...  -38.079584   \n",
              "\n",
              "   2020-12-22  2020-12-23  2020-12-24  2020-12-25  2020-12-26  2020-12-27  \\\n",
              "0 -168.728405 -168.971555 -168.824042 -167.700081 -167.634364 -167.856116   \n",
              "1   72.909418   75.289867   77.770907   74.099941   72.411474   67.472791   \n",
              "2   46.596598   50.135737   48.424880   47.740658   46.676116   45.611939   \n",
              "3  319.281629  322.614999  325.200268  324.745069  326.519830  326.467326   \n",
              "4  -38.515039  -38.031876  -36.610698  -37.753387  -35.470938  -35.454435   \n",
              "\n",
              "   2020-12-28  2020-12-29  2020-12-30  \n",
              "0 -168.743465 -168.231590 -167.913551  \n",
              "1   67.084800   66.805609   72.078232  \n",
              "2   43.331310   45.655325   39.770838  \n",
              "3  321.806523  321.286907  323.570847  \n",
              "4  -35.026979  -35.633306  -35.460477  \n",
              "\n",
              "[5 rows x 730 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-429f5a07-82b7-46bb-8a64-bd780c4f8e1d\" class=\"colab-df-container\">\n",
              "    <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>2019-01-01</th>\n",
              "      <th>2019-01-02</th>\n",
              "      <th>2019-01-03</th>\n",
              "      <th>2019-01-04</th>\n",
              "      <th>2019-01-05</th>\n",
              "      <th>2019-01-06</th>\n",
              "      <th>2019-01-07</th>\n",
              "      <th>2019-01-08</th>\n",
              "      <th>2019-01-09</th>\n",
              "      <th>2019-01-10</th>\n",
              "      <th>...</th>\n",
              "      <th>2020-12-21</th>\n",
              "      <th>2020-12-22</th>\n",
              "      <th>2020-12-23</th>\n",
              "      <th>2020-12-24</th>\n",
              "      <th>2020-12-25</th>\n",
              "      <th>2020-12-26</th>\n",
              "      <th>2020-12-27</th>\n",
              "      <th>2020-12-28</th>\n",
              "      <th>2020-12-29</th>\n",
              "      <th>2020-12-30</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>-3.533155</td>\n",
              "      <td>-4.475755</td>\n",
              "      <td>-4.748074</td>\n",
              "      <td>-5.569953</td>\n",
              "      <td>-8.017896</td>\n",
              "      <td>-12.396771</td>\n",
              "      <td>-11.853961</td>\n",
              "      <td>-12.046498</td>\n",
              "      <td>-10.297042</td>\n",
              "      <td>-10.075553</td>\n",
              "      <td>...</td>\n",
              "      <td>-168.163849</td>\n",
              "      <td>-168.728405</td>\n",
              "      <td>-168.971555</td>\n",
              "      <td>-168.824042</td>\n",
              "      <td>-167.700081</td>\n",
              "      <td>-167.634364</td>\n",
              "      <td>-167.856116</td>\n",
              "      <td>-168.743465</td>\n",
              "      <td>-168.231590</td>\n",
              "      <td>-167.913551</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>-3.824475</td>\n",
              "      <td>-1.530769</td>\n",
              "      <td>0.038338</td>\n",
              "      <td>-1.475237</td>\n",
              "      <td>-1.602029</td>\n",
              "      <td>-0.992509</td>\n",
              "      <td>0.302906</td>\n",
              "      <td>1.789140</td>\n",
              "      <td>4.177589</td>\n",
              "      <td>9.273372</td>\n",
              "      <td>...</td>\n",
              "      <td>75.919063</td>\n",
              "      <td>72.909418</td>\n",
              "      <td>75.289867</td>\n",
              "      <td>77.770907</td>\n",
              "      <td>74.099941</td>\n",
              "      <td>72.411474</td>\n",
              "      <td>67.472791</td>\n",
              "      <td>67.084800</td>\n",
              "      <td>66.805609</td>\n",
              "      <td>72.078232</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>-1.616434</td>\n",
              "      <td>-4.658834</td>\n",
              "      <td>-6.746498</td>\n",
              "      <td>-9.979424</td>\n",
              "      <td>-11.683396</td>\n",
              "      <td>-9.507703</td>\n",
              "      <td>-7.614438</td>\n",
              "      <td>-9.735666</td>\n",
              "      <td>-10.754863</td>\n",
              "      <td>-11.058539</td>\n",
              "      <td>...</td>\n",
              "      <td>47.759469</td>\n",
              "      <td>46.596598</td>\n",
              "      <td>50.135737</td>\n",
              "      <td>48.424880</td>\n",
              "      <td>47.740658</td>\n",
              "      <td>46.676116</td>\n",
              "      <td>45.611939</td>\n",
              "      <td>43.331310</td>\n",
              "      <td>45.655325</td>\n",
              "      <td>39.770838</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>-0.157282</td>\n",
              "      <td>1.827617</td>\n",
              "      <td>-2.585284</td>\n",
              "      <td>-4.397189</td>\n",
              "      <td>0.113494</td>\n",
              "      <td>7.614615</td>\n",
              "      <td>5.456654</td>\n",
              "      <td>5.928451</td>\n",
              "      <td>6.953534</td>\n",
              "      <td>4.514409</td>\n",
              "      <td>...</td>\n",
              "      <td>318.364962</td>\n",
              "      <td>319.281629</td>\n",
              "      <td>322.614999</td>\n",
              "      <td>325.200268</td>\n",
              "      <td>324.745069</td>\n",
              "      <td>326.519830</td>\n",
              "      <td>326.467326</td>\n",
              "      <td>321.806523</td>\n",
              "      <td>321.286907</td>\n",
              "      <td>323.570847</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2.056969</td>\n",
              "      <td>10.187929</td>\n",
              "      <td>8.559520</td>\n",
              "      <td>8.579555</td>\n",
              "      <td>9.464262</td>\n",
              "      <td>9.038088</td>\n",
              "      <td>10.875990</td>\n",
              "      <td>9.883777</td>\n",
              "      <td>8.026472</td>\n",
              "      <td>13.040411</td>\n",
              "      <td>...</td>\n",
              "      <td>-38.079584</td>\n",
              "      <td>-38.515039</td>\n",
              "      <td>-38.031876</td>\n",
              "      <td>-36.610698</td>\n",
              "      <td>-37.753387</td>\n",
              "      <td>-35.470938</td>\n",
              "      <td>-35.454435</td>\n",
              "      <td>-35.026979</td>\n",
              "      <td>-35.633306</td>\n",
              "      <td>-35.460477</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5 rows × 730 columns</p>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-429f5a07-82b7-46bb-8a64-bd780c4f8e1d')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-429f5a07-82b7-46bb-8a64-bd780c4f8e1d button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-429f5a07-82b7-46bb-8a64-bd780c4f8e1d');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-0f6f5d61-0b77-4488-b1c9-0704a10de81c\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-0f6f5d61-0b77-4488-b1c9-0704a10de81c')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-0f6f5d61-0b77-4488-b1c9-0704a10de81c button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df"
            }
          },
          "metadata": {},
          "execution_count": 3
        }
      ]
    },
    {
      "cell_type": "markdown",
      "id": "acaec743",
      "metadata": {
        "id": "acaec743"
      },
      "source": [
        "## Напишите следующие функции"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "id": "7a7e22f0-5ee0-48dc-9191-38d17bb25853",
      "metadata": {
        "id": "7a7e22f0-5ee0-48dc-9191-38d17bb25853"
      },
      "outputs": [],
      "source": [
        "def generate_groups(df, group_num):\n",
        "    \"\"\"\n",
        "    Splits users into group_num groups of similar size\n",
        "\n",
        "    df : Dataframe with all user data\n",
        "    group_num : number of groups to split into\n",
        "\n",
        "    returns a list of dataframes of generated groups\n",
        "    \"\"\"\n",
        "    # permutations обеспечит случайность выбора наших групп, поэтому основные описательные статистики между группами будут равны при большом числе наблюдений, и группы будут похожими\n",
        "    n_users = len(df)\n",
        "    indexes_to_choose = np.random.permutation(n_users) # Создадим последовательность перестановок длины n\n",
        "    group_indexes = np.array_split(indexes_to_choose, group_num) # Поделим данные на равные группы, получим списки индексов для каждой группы (список списков)\n",
        "    groups = []\n",
        "    for indexes in group_indexes:\n",
        "        group = df.iloc[indexes]\n",
        "        groups.append(group)\n",
        "    return groups\n",
        "\n",
        "# def generate_groups(data, num_groups):\n",
        "#     shuffled_data = data.sample(frac=1) # перемешаем пользователей\n",
        "#     groups = [pd.DataFrame() for _ in range(num_groups)] # создаем num_groups df\n",
        "\n",
        "#     while len(shuffled_data) > 0: # Пока пользователи не кончились\n",
        "#         best_cost_reduction = 100000000000 # макс значение ставим, чтобы потом с ним сравнивать\n",
        "#         best_group = None\n",
        "#         best_candidate_idx = None\n",
        "\n",
        "#         # Находим пару наблюдений, которая максимально уменьшает стоимость\n",
        "#         for i, group in enumerate(groups):  # -> (0,0), (1,1), (2,2)\n",
        "#             for j, candidate_idx in enumerate(shuffled_data.index):\n",
        "#                 candidate = shuffled_data.loc[[candidate_idx]] # берем челика\n",
        "#                 group_combined = pd.concat([group, candidate]) # добавляем в группу\n",
        "#                 cost_reduction = calculate_cost_reduction(groups, group_combined) # считаем функцию потерь\n",
        "\n",
        "#                 if cost_reduction < best_cost_reduction: # Обновляем настройки, если у новой группы функция потерь меньше, чем у прошлых\n",
        "#                     best_cost_reduction = cost_reduction\n",
        "#                     best_group = i\n",
        "#                     best_candidate_idx = candidate_idx\n",
        "\n",
        "#         # Как нашли два челика, раскидываем их по группам\n",
        "#         groups[best_group] = pd.concat([groups[best_group], shuffled_data.loc[[best_candidate_idx]]]) # добавляем нового найденного челика в группу\n",
        "#         shuffled_data = shuffled_data.drop(best_candidate_idx) # и удаляем распределенного в группы челика, повторяем, пока shuffled_data не станет пустым\n",
        "\n",
        "#     return groups\n",
        "\n",
        "# # Функция для вычисления изменения стоимости при добавлении наблюдения в группу\n",
        "# def calculate_cost_reduction(groups, group_combined):\n",
        "#     # Рассчитываем изменение стоимости как сумму различий между группами\n",
        "#     cost_reduction = 0\n",
        "#     for group in groups:\n",
        "#         cost_reduction += abs(group_combined.mean(axis=1) - group.mean(axis=1)) + abs(group_combined.var(axis=1) - group.var(axis=1))\n",
        "#     return cost_reduction\n",
        "\n",
        "\n",
        "\n",
        "# # Генерируем 3 группы\n",
        "# generated_groups = generate_groups(df, 10)\n",
        "\n",
        "# np.random.seed(123)\n",
        "def conduct_tests_on_pair(df_1, df_2):\n",
        "    \"\"\"\n",
        "    Conducts statistical test or tests on two given groups to decide if they are similar\n",
        "\n",
        "    df_i : Dataframe of users of a single group\n",
        "\n",
        "    returns p-value or anything you need further\n",
        "    \"\"\"\n",
        "    means_df_1 = df_1.mean(axis=1)  # Сначала посчитаем среднее значение по времени для каждого пользователя в каждой группе, потом сравним средние значения пользователей двух групп между собой на равенство мат ожидания двух групп\n",
        "    means_df_2 = df_2.mean(axis=1)\n",
        "\n",
        "    var_df_1 = df_1.var(axis=1) # То же самое с дисперсиями\n",
        "    var_df_2 = df_2.var(axis=1)\n",
        "\n",
        "    stat_tt, p_tt = stats.ttest_ind(means_df_1, means_df_2)\n",
        "    stat_mw, p_mw = mannwhitneyu(means_df_1, means_df_2)\n",
        "    stat_lev, p_lev = levene(var_df_1, var_df_2)\n",
        "\n",
        "    # print(f'Двусторонний t-тест, тестовая статистика: {stat_tt}, p-value: {p_tt}')\n",
        "    # print(f'Тест Mann-Witney, тестовая статистика: {stat_mw}, p-value: {p_mw}')\n",
        "    # print(f'Тест Левене, тестовая статистика: {stat_lev}, p-value: {p_lev}')\n",
        "    return stat_tt, p_tt, stat_mw, p_mw, stat_lev, p_lev\n",
        "\n",
        "\n",
        "# print(conduct_tests_on_pair(generated_groups[0], generated_groups[1]))\n",
        "\n",
        "def conduct_tests(dfs, group_num):\n",
        "    \"\"\"\n",
        "    Goal is to find out if there is group_num groups in the dfs list which are similar enough according to your criterion.\n",
        "    For every (or maybe not every) selection of group_num groups from the dfs list and conducts pairwise tests on selected subset using conduct_tests_on_pair.\n",
        "\n",
        "    dfs : list of dataframes of groups\n",
        "\n",
        "    returns list of selected groups or None if there are no similar groups\n",
        "    \"\"\"\n",
        "    group_combinations = combinations(dfs, group_num) # комбинации групп для проверки\n",
        "    for combination in group_combinations:\n",
        "        # Стат тесты для проверки похожести: левене для проверки схожести дисперсий\n",
        "        p_values_tt = []\n",
        "        p_values_mw = []\n",
        "        p_values_lev = []\n",
        "        for df1, df2 in combinations(combination, 2):\n",
        "            tt_stat, p_tt, mw_stat, p_mw, lev_stat, p_lev = conduct_tests_on_pair(df1, df2)\n",
        "            p_values_tt.append(p_tt)\n",
        "            p_values_mw.append(p_mw)\n",
        "            p_values_lev.append(p_lev)\n",
        "\n",
        "        # Будем требовать, чтобы все тесты не отвергались, уровень значимости возьмем побольше, чтобы группы были более похожими\n",
        "        if all(p > 0.2 for p in p_values_tt) and all(p > 0.2 for p in p_values_mw) and all(p > 0.2 for p in p_values_lev):\n",
        "            return list(combination)\n",
        "    return None\n",
        "\n",
        "def find_groups(df, group_num):\n",
        "    \"\"\"\n",
        "    Splits df into groups and conducts tests on subsets of the groups until necessary groups are found\n",
        "    \"\"\"\n",
        "    for i in range(100000):\n",
        "        dfs = generate_groups(df, group_num)\n",
        "        result = conduct_tests(dfs, 3)\n",
        "\n",
        "        if result is not None:\n",
        "            return result"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "5ec0bb93-2187-43ff-9a9c-e34eb3c01240",
      "metadata": {
        "id": "5ec0bb93-2187-43ff-9a9c-e34eb3c01240"
      },
      "outputs": [],
      "source": [
        "# df = pd.read_csv('train_data.csv')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "id": "0cc557a0-3d1b-488c-8737-0234e4436e23",
      "metadata": {
        "id": "0cc557a0-3d1b-488c-8737-0234e4436e23"
      },
      "outputs": [],
      "source": [
        "group_num = 10"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "id": "3642b77e-a191-4048-9544-64afb8131308",
      "metadata": {
        "tags": [],
        "id": "3642b77e-a191-4048-9544-64afb8131308"
      },
      "outputs": [],
      "source": [
        "groups = find_groups(df, group_num)"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# for sample in groups:\n",
        "#   print(sample)\n",
        "#   print('-'*100 + '\\n' + '-'*100)"
      ],
      "metadata": {
        "id": "zx8oOORucRe6"
      },
      "id": "zx8oOORucRe6",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "id": "72deb6ca-1a40-4791-a8e9-cc6db1cbf5b6",
      "metadata": {
        "id": "72deb6ca-1a40-4791-a8e9-cc6db1cbf5b6"
      },
      "source": [
        "# Visualize groups"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "ab8aa101-49da-44ac-b469-49e87a37ddbf",
      "metadata": {
        "id": "ab8aa101-49da-44ac-b469-49e87a37ddbf"
      },
      "source": [
        "Тут секция, где вам нужно визуализировать распределения выбранных вами метрик для найденных групп и сравнить с аналогичными разультатами для случайных групп"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "90eeec51-b333-427b-8f23-3830179652dc",
      "metadata": {
        "id": "90eeec51-b333-427b-8f23-3830179652dc"
      },
      "outputs": [],
      "source": [
        "groups_random = generate_groups(df, 10)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 38,
      "id": "e7e5e023-ecce-4855-8894-b6d75eafa35f",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 430
        },
        "id": "e7e5e023-ecce-4855-8894-b6d75eafa35f",
        "outputId": "d3c830ba-abaf-4679-82f2-bbbac21ffc4f"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "groups = find_groups(df, 10)\n",
        "x1 = np.linspace(-1, 1, 100)\n",
        "y1 = groups[0].mean(axis=1)\n",
        "y2 = groups[1].mean(axis=1)\n",
        "y3 = groups[2].mean(axis=1)\n",
        "fig, ax = plt.subplots(1, 1)\n",
        "ax.hist(y1, density=True, histtype='bar', bins=300, alpha=0.3, color='mediumorchid')\n",
        "ax.hist(y2, density=True, histtype='bar', bins=300, alpha=0.7, color='cyan')\n",
        "ax.hist(y3, density=True, histtype='bar', bins=300, alpha=0.9, color='red')\n",
        "ax.axvline(np.mean(y1), ymax=1, color='black', alpha=0.8)\n",
        "ax.axvline(np.mean(y2), ymax=1, color='green', alpha=0.8)\n",
        "ax.axvline(np.mean(y3), ymax=1, color='yellow', alpha=0.8)\n",
        "# rect = matplotlib.patches.Rectangle((np.mean(y1)-3*np.std(y1),0), 6*np.std(y1), 0.3, color='black', alpha=0.2)\n",
        "# ax.add_patch(rect)\n",
        "plt.xlim(-5000, 3000)\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "fig, ax = plt.subplots(1, 1)\n",
        "rect1 = matplotlib.patches.Rectangle((np.mean(y1)-np.std(y1),0), 2*np.std(y1), 0.7, color='black', alpha=0.25)\n",
        "rect2 = matplotlib.patches.Rectangle((np.mean(y2)-np.std(y2),0), 2*np.std(y2), 0.7, color='red', alpha=0.3)\n",
        "rect3 = matplotlib.patches.Rectangle((np.mean(y3)-np.std(y3),0), 2*np.std(y3), 0.7, color='cyan', alpha=0.35)\n",
        "ax.add_patch(rect1)\n",
        "ax.add_patch(rect2)\n",
        "ax.add_patch(rect3)\n",
        "plt.xlim(-20000, 20000)\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 435
        },
        "id": "2lfkyGZzqDmm",
        "outputId": "897785ab-3f50-4126-d72a-7ab913d22e15"
      },
      "id": "2lfkyGZzqDmm",
      "execution_count": 39,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Видим, что средние и дисперсии примерно равны, потому что мы их так строили."
      ],
      "metadata": {
        "id": "box5AroqzSJT"
      },
      "id": "box5AroqzSJT"
    },
    {
      "cell_type": "code",
      "source": [
        "simulations = 10000\n",
        "n_s = 50\n",
        "res = []\n",
        "from tqdm.auto import tqdm\n",
        "\n",
        "# Запуск симуляций A/A теста\n",
        "for i in tqdm(range(simulations)):\n",
        "  s1 = y1.sample(n_s, replace = False).values\n",
        "  s2 = y3.sample(n_s, replace = False).values\n",
        "  res.append(stats.ttest_ind(s1, s2, equal_var = True)[1]) # сохраняем pvalue\n",
        "\n",
        "plt.hist(res, bins = 50)\n",
        "plt.style.use('ggplot')\n",
        "plt.xlabel('pvalues')\n",
        "plt.ylabel('frequency')\n",
        "plt.title(\"Histogram of ttest A/A simulations \")\n",
        "plt.show()\n",
        "\n",
        "# Проверяем, что количество ложноположительных случаев не превышает альфа\n",
        "sum(np.array(res) < 0.05) / simulations"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 527,
          "referenced_widgets": [
            "dd452506d43c4e869dfe3edab93480b9",
            "931cd526510f44b1ae8e7242d282a658",
            "b724419dd9834203afbbef5990d4582e",
            "82b4e11fce284aeda47ee49738dbe052",
            "604ff40588aa407caead97b70579c7d3",
            "85a79ac28a534a7f9611a6d9fe15d563",
            "3cc47fb72e6d4943b58d922e3103836a",
            "b259f338d1a8423b8f086eff45a1017f",
            "c9eed446877f4dfcb6c1645bde6a8ed8",
            "76e3b598c34a4f38b2f12b087be065ad",
            "fae9a523ff5141698f11df80d2785de4"
          ]
        },
        "id": "938YFPcLvCri",
        "outputId": "b31831e0-1580-4d3f-a7d1-8b2c1d45320f"
      },
      "id": "938YFPcLvCri",
      "execution_count": 40,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/10000 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "dd452506d43c4e869dfe3edab93480b9"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.027"
            ]
          },
          "metadata": {},
          "execution_count": 40
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "При этом распределение p-value неравномерное. Это может быть связано с тем, что есть другие характеристики распределения данных, которые мы не учли в своем анализе. Проверим с помощью теста, чтобы убедиться:"
      ],
      "metadata": {
        "id": "wWxL7xpXzun1"
      },
      "id": "wWxL7xpXzun1"
    },
    {
      "cell_type": "code",
      "source": [
        "from scipy.stats import kstest, uniform\n",
        "\n",
        "def is_uniform(sample, significance_level):\n",
        "    # Perform the Kolmogorov-Smirnov test\n",
        "    _, p_value = kstest(sample, uniform.cdf)\n",
        "\n",
        "    return p_value\n",
        "\n",
        "significance_level = 0.05\n",
        "# Example usage\n",
        "p_value = is_uniform(res, significance_level)\n",
        "\n",
        "if p_value > significance_level:\n",
        "    print(\"Uniform\")  # Sample distribution is likely uniform\n",
        "else:\n",
        "    print(\"Not Uniform\")  # Sample distribution is not likely uniform\n",
        "\n",
        "p_value"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3dm26uSDIGsw",
        "outputId": "31f9c055-08f6-4977-c2a4-eb932f8ea7d7"
      },
      "id": "3dm26uSDIGsw",
      "execution_count": 41,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Not Uniform\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1.492651484311756e-116"
            ]
          },
          "metadata": {},
          "execution_count": 41
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Для y1, y2 и y2, y3 соотношение примерно такое же"
      ],
      "metadata": {
        "id": "iTe_kr2cz22t"
      },
      "id": "iTe_kr2cz22t"
    },
    {
      "cell_type": "code",
      "source": [
        "simulations = 10000\n",
        "n_s = 50\n",
        "res = []\n",
        "from tqdm.auto import tqdm\n",
        "\n",
        "# Запуск симуляций A/A теста\n",
        "for i in tqdm(range(simulations)):\n",
        "  s1 = y1.sample(n_s, replace = False).values\n",
        "  s2 = y2.sample(n_s, replace = False).values\n",
        "  res.append(stats.ttest_ind(s1, s2, equal_var = True)[1]) # сохраняем pvalue\n",
        "\n",
        "plt.hist(res, bins = 50)\n",
        "plt.style.use('ggplot')\n",
        "plt.xlabel('pvalues')\n",
        "plt.ylabel('frequency')\n",
        "plt.title(\"Histogram of ttest A/A simulations \")\n",
        "plt.show()\n",
        "\n",
        "# Проверяем, что количество ложноположительных случаев не превышает альфа\n",
        "sum(np.array(res) < 0.05) / simulations"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 527,
          "referenced_widgets": [
            "0979f9921bb64f8cbf20b5c1240deb88",
            "e81056a3082242b188fbe376c48d42ea",
            "09b360a2b48b4f7ba662c8d2202d8351",
            "ae8a319d9a0d4f5cac211fb0763de400",
            "3af29bce0caa425ab953d03ff8aa4034",
            "f68b63a7346945a5a15a08ee6f45dcf9",
            "890298022bb644bc9237c8d511023340",
            "aa1b974f234349ee8a6a51455372260f",
            "7cf0472df1e0401ca206c73f943ad39c",
            "80a4bdc04c45489bb31b2f08c0a773a1",
            "7f59ce2eea2541efbb3dd58049af3eed"
          ]
        },
        "id": "OxnPoS3q1UZx",
        "outputId": "1f498ac4-d871-4173-c9fe-31ff2d504ae5"
      },
      "id": "OxnPoS3q1UZx",
      "execution_count": 42,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/10000 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "0979f9921bb64f8cbf20b5c1240deb88"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.0295"
            ]
          },
          "metadata": {},
          "execution_count": 42
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "simulations = 10000\n",
        "n_s = 50\n",
        "res = []\n",
        "from tqdm.auto import tqdm\n",
        "\n",
        "# Запуск симуляций A/A теста\n",
        "for i in tqdm(range(simulations)):\n",
        "  s1 = y2.sample(n_s, replace = False).values\n",
        "  s2 = y3.sample(n_s, replace = False).values\n",
        "  res.append(stats.ttest_ind(s1, s2, equal_var = True)[1]) # сохраняем pvalue\n",
        "\n",
        "plt.hist(res, bins = 50)\n",
        "plt.style.use('ggplot')\n",
        "plt.xlabel('pvalues')\n",
        "plt.ylabel('frequency')\n",
        "plt.title(\"Histogram of ttest A/A simulations \")\n",
        "plt.show()\n",
        "\n",
        "# Проверяем, что количество ложноположительных случаев не превышает альфа\n",
        "sum(np.array(res) < 0.05) / simulations"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 527,
          "referenced_widgets": [
            "0cc06720c44f4f9e93653e74f94bdea5",
            "553066a3b0a842d382b4201ab01a31ea",
            "82b8247bfcc34da89cbad5c7d369008b",
            "99cdb0a611794e0a88f1b321f0d90ba6",
            "1ea7dbd542db43c2a9cc4791e230fe2d",
            "ad09c5bdb7c84ac4971091376493a015",
            "0a1e7266f4e44863add8170393923b98",
            "4c841b365e8b4ec49e1e7420ac42a68e",
            "953cae3598044c56b09d1fceabd2e59e",
            "3c5d4eeb526e41e9b964f6ba32dc0997",
            "aebe1e5f6f604694bea3df7109b1b16d"
          ]
        },
        "id": "Wnj22YJ91WnL",
        "outputId": "e2ebbd93-904c-428e-8c48-cdbcbc35206e"
      },
      "id": "Wnj22YJ91WnL",
      "execution_count": 43,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/10000 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "0cc06720c44f4f9e93653e74f94bdea5"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.0306"
            ]
          },
          "metadata": {},
          "execution_count": 43
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Теперь сравним со случайной выборкой трех групп:"
      ],
      "metadata": {
        "id": "Aw2dndUPz_yl"
      },
      "id": "Aw2dndUPz_yl"
    },
    {
      "cell_type": "code",
      "source": [
        "# df = pd.read_csv(data, delimiter=',')\n",
        "y1 = df.iloc[:5000, :].mean(axis=1)\n",
        "y2 = df.iloc[5000:10000, :].mean(axis=1)\n",
        "y3 = df.iloc[10000:15000, :].mean(axis=1)"
      ],
      "metadata": {
        "id": "oqwgJuffX918"
      },
      "id": "oqwgJuffX918",
      "execution_count": 18,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "x1 = np.linspace(-1, 1, 100)\n",
        "fig, ax = plt.subplots(1, 1)\n",
        "ax.hist(y1, density=True, histtype='bar', bins=300, alpha=0.3, color='mediumorchid')\n",
        "ax.hist(y2, density=True, histtype='bar', bins=300, alpha=0.7, color='cyan')\n",
        "ax.hist(y3, density=True, histtype='bar', bins=300, alpha=0.9, color='red')\n",
        "ax.axvline(np.mean(y1), ymax=1, color='black', alpha=0.8)\n",
        "ax.axvline(np.mean(y2), ymax=1, color='green', alpha=0.8)\n",
        "ax.axvline(np.mean(y3), ymax=1, color='yellow', alpha=0.8)\n",
        "# rect = matplotlib.patches.Rectangle((np.mean(y1)-3*np.std(y1),0), 6*np.std(y1), 0.3, color='black', alpha=0.2)\n",
        "# ax.add_patch(rect)\n",
        "plt.xlim(-5000, 3000)\n",
        "\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 430
        },
        "id": "XPONTtZuYqjC",
        "outputId": "12dda3af-ac66-4da0-c102-da0c44f5c0d5"
      },
      "id": "XPONTtZuYqjC",
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "fig, ax = plt.subplots(1, 1)\n",
        "rect1 = matplotlib.patches.Rectangle((np.mean(y1)-np.std(y1),0), 2*np.std(y1), 0.7, color='black', alpha=0.25)\n",
        "rect2 = matplotlib.patches.Rectangle((np.mean(y2)-np.std(y2),0), 2*np.std(y2), 0.7, color='red', alpha=0.3)\n",
        "rect3 = matplotlib.patches.Rectangle((np.mean(y3)-np.std(y3),0), 2*np.std(y3), 0.7, color='cyan', alpha=0.35)\n",
        "ax.add_patch(rect1)\n",
        "ax.add_patch(rect2)\n",
        "ax.add_patch(rect3)\n",
        "plt.xlim(-20000, 20000)\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 435
        },
        "id": "_DOak2QDZuPT",
        "outputId": "1160a6f9-0654-48ed-87ad-14a02cfe52ae"
      },
      "id": "_DOak2QDZuPT",
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Матожидания тоже совпали, но дисперсии при этом немного отличаются"
      ],
      "metadata": {
        "id": "SaOcsrUq0HoC"
      },
      "id": "SaOcsrUq0HoC"
    },
    {
      "cell_type": "code",
      "source": [
        "# n = 100_000\n",
        "simulations = 10000\n",
        "n_s = 50\n",
        "res = []\n",
        "from tqdm.auto import tqdm\n",
        "\n",
        "# Запуск симуляций A/A теста\n",
        "for i in tqdm(range(simulations)):\n",
        "  s1 = y1.sample(n_s, replace = False).values\n",
        "  s2 = y2.sample(n_s, replace = False).values\n",
        "  res.append(stats.ttest_ind(s1, s2, equal_var = False)[1]) # сохраняем pvalue\n",
        "\n",
        "plt.hist(res, bins = 50)\n",
        "plt.style.use('ggplot')\n",
        "plt.xlabel('pvalues')\n",
        "plt.ylabel('frequency')\n",
        "plt.title(\"Histogram of ttest A/A simulations \")\n",
        "plt.show()\n",
        "\n",
        "# Проверяем, что количество ложноположительных случаев не превышает альфа\n",
        "sum(np.array(res) < 0.05) / simulations"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 527,
          "referenced_widgets": [
            "4ee7077afe4f43d08d7a6c81c66a6f75",
            "bb46bf33d2ba4377842e720756eed45d",
            "d133b13a94c94fbe916f94f4bd6addf9",
            "209dce02cf7f4a52b95c1d74f2c550d6",
            "e3bc1328aa4d43ada61e96f437999a6a",
            "bf1d9e4e0ac34f6f865942b893734ab1",
            "37e3990333024ddaa9163f6aa979d8a7",
            "07ea23db87034b1d84cf0a1b3e844268",
            "f84f99ec6d42463a9e0cea727d9d31a3",
            "e42dd3b85aaa453a9252fbf4af76f20d",
            "4e1e3f0992004ddb94f478da48971554"
          ]
        },
        "id": "FXrsJVYhZzUs",
        "outputId": "7b0ccc1d-2532-4845-ef92-b8536942d47d"
      },
      "id": "FXrsJVYhZzUs",
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/10000 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "4ee7077afe4f43d08d7a6c81c66a6f75"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.0278"
            ]
          },
          "metadata": {},
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Распределение p-value неравномерное, что подтверждается тестом:"
      ],
      "metadata": {
        "id": "ehaiTyIh0OP3"
      },
      "id": "ehaiTyIh0OP3"
    },
    {
      "cell_type": "code",
      "source": [
        "from scipy.stats import kstest, uniform\n",
        "\n",
        "def is_uniform(sample, significance_level):\n",
        "    # Perform the Kolmogorov-Smirnov test\n",
        "    _, p_value = kstest(sample, uniform.cdf)\n",
        "\n",
        "    return p_value\n",
        "\n",
        "significance_level = 0.05\n",
        "# Example usage\n",
        "p_value = is_uniform(res, significance_level)\n",
        "\n",
        "if p_value > significance_level:\n",
        "    print(\"Uniform\")  # Sample distribution is likely uniform\n",
        "else:\n",
        "    print(\"Not Uniform\")  # Sample distribution is not likely uniform\n",
        "\n",
        "p_value"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "MAJ8zelGaEVT",
        "outputId": "13453fbc-f9c7-4c27-b969-37edd9d41a57"
      },
      "id": "MAJ8zelGaEVT",
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Not Uniform\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "4.05287317324593e-88"
            ]
          },
          "metadata": {},
          "execution_count": 22
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Распределение неравномерное."
      ],
      "metadata": {
        "id": "3_pgRtZP0Vby"
      },
      "id": "3_pgRtZP0Vby"
    },
    {
      "cell_type": "markdown",
      "source": [
        "Результат говорит о том, что сплит-система отрабатывает не совсем корректно.\n",
        "Я пытался построить жадный алгоритм отбора пользователей в группы, но, к сожалению, так и не смог реализовать полностью его. Жадный алгоритм дал бы более хорошие результаты, потому что сам метод основан на том, что мы отбираем пользователей, исходя из минимизации разности их метрик (матожидания и дисперсии) и получаем во всех группах пользователей с одинаковым распределением этих метрик."
      ],
      "metadata": {
        "id": "4Vba7KGm0Ypb"
      },
      "id": "4Vba7KGm0Ypb"
    }
  ],
  "metadata": {
    "kernelspec": {
      "display_name": "Python 3",
      "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": "3.11.4"
    },
    "colab": {
      "provenance": [],
      "gpuType": "T4"
    },
    "widgets": {
      "application/vnd.jupyter.widget-state+json": {
        "dd452506d43c4e869dfe3edab93480b9": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "HBoxModel",
          "model_module_version": "1.5.0",
          "state": {
            "_dom_classes": [],
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "HBoxModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/controls",
            "_view_module_version": "1.5.0",
            "_view_name": "HBoxView",
            "box_style": "",
            "children": [
              "IPY_MODEL_931cd526510f44b1ae8e7242d282a658",
              "IPY_MODEL_b724419dd9834203afbbef5990d4582e",
              "IPY_MODEL_82b4e11fce284aeda47ee49738dbe052"
            ],
            "layout": "IPY_MODEL_604ff40588aa407caead97b70579c7d3"
          }
        },
        "931cd526510f44b1ae8e7242d282a658": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "HTMLModel",
          "model_module_version": "1.5.0",
          "state": {
            "_dom_classes": [],
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "HTMLModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/controls",
            "_view_module_version": "1.5.0",
            "_view_name": "HTMLView",
            "description": "",
            "description_tooltip": null,
            "layout": "IPY_MODEL_85a79ac28a534a7f9611a6d9fe15d563",
            "placeholder": "​",
            "style": "IPY_MODEL_3cc47fb72e6d4943b58d922e3103836a",
            "value": "100%"
          }
        },
        "b724419dd9834203afbbef5990d4582e": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "FloatProgressModel",
          "model_module_version": "1.5.0",
          "state": {
            "_dom_classes": [],
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "FloatProgressModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/controls",
            "_view_module_version": "1.5.0",
            "_view_name": "ProgressView",
            "bar_style": "success",
            "description": "",
            "description_tooltip": null,
            "layout": "IPY_MODEL_b259f338d1a8423b8f086eff45a1017f",
            "max": 10000,
            "min": 0,
            "orientation": "horizontal",
            "style": "IPY_MODEL_c9eed446877f4dfcb6c1645bde6a8ed8",
            "value": 10000
          }
        },
        "82b4e11fce284aeda47ee49738dbe052": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "HTMLModel",
          "model_module_version": "1.5.0",
          "state": {
            "_dom_classes": [],
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "HTMLModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/controls",
            "_view_module_version": "1.5.0",
            "_view_name": "HTMLView",
            "description": "",
            "description_tooltip": null,
            "layout": "IPY_MODEL_76e3b598c34a4f38b2f12b087be065ad",
            "placeholder": "​",
            "style": "IPY_MODEL_fae9a523ff5141698f11df80d2785de4",
            "value": " 10000/10000 [00:12&lt;00:00, 637.67it/s]"
          }
        },
        "604ff40588aa407caead97b70579c7d3": {
          "model_module": "@jupyter-widgets/base",
          "model_name": "LayoutModel",
          "model_module_version": "1.2.0",
          "state": {
            "_model_module": "@jupyter-widgets/base",
            "_model_module_version": "1.2.0",
            "_model_name": "LayoutModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "LayoutView",
            "align_content": null,
            "align_items": null,
            "align_self": null,
            "border": null,
            "bottom": null,
            "display": null,
            "flex": null,
            "flex_flow": null,
            "grid_area": null,
            "grid_auto_columns": null,
            "grid_auto_flow": null,
            "grid_auto_rows": null,
            "grid_column": null,
            "grid_gap": null,
            "grid_row": null,
            "grid_template_areas": null,
            "grid_template_columns": null,
            "grid_template_rows": null,
            "height": null,
            "justify_content": null,
            "justify_items": null,
            "left": null,
            "margin": null,
            "max_height": null,
            "max_width": null,
            "min_height": null,
            "min_width": null,
            "object_fit": null,
            "object_position": null,
            "order": null,
            "overflow": null,
            "overflow_x": null,
            "overflow_y": null,
            "padding": null,
            "right": null,
            "top": null,
            "visibility": null,
            "width": null
          }
        },
        "85a79ac28a534a7f9611a6d9fe15d563": {
          "model_module": "@jupyter-widgets/base",
          "model_name": "LayoutModel",
          "model_module_version": "1.2.0",
          "state": {
            "_model_module": "@jupyter-widgets/base",
            "_model_module_version": "1.2.0",
            "_model_name": "LayoutModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "LayoutView",
            "align_content": null,
            "align_items": null,
            "align_self": null,
            "border": null,
            "bottom": null,
            "display": null,
            "flex": null,
            "flex_flow": null,
            "grid_area": null,
            "grid_auto_columns": null,
            "grid_auto_flow": null,
            "grid_auto_rows": null,
            "grid_column": null,
            "grid_gap": null,
            "grid_row": null,
            "grid_template_areas": null,
            "grid_template_columns": null,
            "grid_template_rows": null,
            "height": null,
            "justify_content": null,
            "justify_items": null,
            "left": null,
            "margin": null,
            "max_height": null,
            "max_width": null,
            "min_height": null,
            "min_width": null,
            "object_fit": null,
            "object_position": null,
            "order": null,
            "overflow": null,
            "overflow_x": null,
            "overflow_y": null,
            "padding": null,
            "right": null,
            "top": null,
            "visibility": null,
            "width": null
          }
        },
        "3cc47fb72e6d4943b58d922e3103836a": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "DescriptionStyleModel",
          "model_module_version": "1.5.0",
          "state": {
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "DescriptionStyleModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "StyleView",
            "description_width": ""
          }
        },
        "b259f338d1a8423b8f086eff45a1017f": {
          "model_module": "@jupyter-widgets/base",
          "model_name": "LayoutModel",
          "model_module_version": "1.2.0",
          "state": {
            "_model_module": "@jupyter-widgets/base",
            "_model_module_version": "1.2.0",
            "_model_name": "LayoutModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "LayoutView",
            "align_content": null,
            "align_items": null,
            "align_self": null,
            "border": null,
            "bottom": null,
            "display": null,
            "flex": null,
            "flex_flow": null,
            "grid_area": null,
            "grid_auto_columns": null,
            "grid_auto_flow": null,
            "grid_auto_rows": null,
            "grid_column": null,
            "grid_gap": null,
            "grid_row": null,
            "grid_template_areas": null,
            "grid_template_columns": null,
            "grid_template_rows": null,
            "height": null,
            "justify_content": null,
            "justify_items": null,
            "left": null,
            "margin": null,
            "max_height": null,
            "max_width": null,
            "min_height": null,
            "min_width": null,
            "object_fit": null,
            "object_position": null,
            "order": null,
            "overflow": null,
            "overflow_x": null,
            "overflow_y": null,
            "padding": null,
            "right": null,
            "top": null,
            "visibility": null,
            "width": null
          }
        },
        "c9eed446877f4dfcb6c1645bde6a8ed8": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "ProgressStyleModel",
          "model_module_version": "1.5.0",
          "state": {
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "ProgressStyleModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "StyleView",
            "bar_color": null,
            "description_width": ""
          }
        },
        "76e3b598c34a4f38b2f12b087be065ad": {
          "model_module": "@jupyter-widgets/base",
          "model_name": "LayoutModel",
          "model_module_version": "1.2.0",
          "state": {
            "_model_module": "@jupyter-widgets/base",
            "_model_module_version": "1.2.0",
            "_model_name": "LayoutModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "LayoutView",
            "align_content": null,
            "align_items": null,
            "align_self": null,
            "border": null,
            "bottom": null,
            "display": null,
            "flex": null,
            "flex_flow": null,
            "grid_area": null,
            "grid_auto_columns": null,
            "grid_auto_flow": null,
            "grid_auto_rows": null,
            "grid_column": null,
            "grid_gap": null,
            "grid_row": null,
            "grid_template_areas": null,
            "grid_template_columns": null,
            "grid_template_rows": null,
            "height": null,
            "justify_content": null,
            "justify_items": null,
            "left": null,
            "margin": null,
            "max_height": null,
            "max_width": null,
            "min_height": null,
            "min_width": null,
            "object_fit": null,
            "object_position": null,
            "order": null,
            "overflow": null,
            "overflow_x": null,
            "overflow_y": null,
            "padding": null,
            "right": null,
            "top": null,
            "visibility": null,
            "width": null
          }
        },
        "fae9a523ff5141698f11df80d2785de4": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "DescriptionStyleModel",
          "model_module_version": "1.5.0",
          "state": {
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "DescriptionStyleModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "StyleView",
            "description_width": ""
          }
        },
        "0979f9921bb64f8cbf20b5c1240deb88": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "HBoxModel",
          "model_module_version": "1.5.0",
          "state": {
            "_dom_classes": [],
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "HBoxModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/controls",
            "_view_module_version": "1.5.0",
            "_view_name": "HBoxView",
            "box_style": "",
            "children": [
              "IPY_MODEL_e81056a3082242b188fbe376c48d42ea",
              "IPY_MODEL_09b360a2b48b4f7ba662c8d2202d8351",
              "IPY_MODEL_ae8a319d9a0d4f5cac211fb0763de400"
            ],
            "layout": "IPY_MODEL_3af29bce0caa425ab953d03ff8aa4034"
          }
        },
        "e81056a3082242b188fbe376c48d42ea": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "HTMLModel",
          "model_module_version": "1.5.0",
          "state": {
            "_dom_classes": [],
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "HTMLModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/controls",
            "_view_module_version": "1.5.0",
            "_view_name": "HTMLView",
            "description": "",
            "description_tooltip": null,
            "layout": "IPY_MODEL_f68b63a7346945a5a15a08ee6f45dcf9",
            "placeholder": "​",
            "style": "IPY_MODEL_890298022bb644bc9237c8d511023340",
            "value": "100%"
          }
        },
        "09b360a2b48b4f7ba662c8d2202d8351": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "FloatProgressModel",
          "model_module_version": "1.5.0",
          "state": {
            "_dom_classes": [],
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "FloatProgressModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/controls",
            "_view_module_version": "1.5.0",
            "_view_name": "ProgressView",
            "bar_style": "success",
            "description": "",
            "description_tooltip": null,
            "layout": "IPY_MODEL_aa1b974f234349ee8a6a51455372260f",
            "max": 10000,
            "min": 0,
            "orientation": "horizontal",
            "style": "IPY_MODEL_7cf0472df1e0401ca206c73f943ad39c",
            "value": 10000
          }
        },
        "ae8a319d9a0d4f5cac211fb0763de400": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "HTMLModel",
          "model_module_version": "1.5.0",
          "state": {
            "_dom_classes": [],
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "HTMLModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/controls",
            "_view_module_version": "1.5.0",
            "_view_name": "HTMLView",
            "description": "",
            "description_tooltip": null,
            "layout": "IPY_MODEL_80a4bdc04c45489bb31b2f08c0a773a1",
            "placeholder": "​",
            "style": "IPY_MODEL_7f59ce2eea2541efbb3dd58049af3eed",
            "value": " 10000/10000 [00:13&lt;00:00, 575.19it/s]"
          }
        },
        "3af29bce0caa425ab953d03ff8aa4034": {
          "model_module": "@jupyter-widgets/base",
          "model_name": "LayoutModel",
          "model_module_version": "1.2.0",
          "state": {
            "_model_module": "@jupyter-widgets/base",
            "_model_module_version": "1.2.0",
            "_model_name": "LayoutModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "LayoutView",
            "align_content": null,
            "align_items": null,
            "align_self": null,
            "border": null,
            "bottom": null,
            "display": null,
            "flex": null,
            "flex_flow": null,
            "grid_area": null,
            "grid_auto_columns": null,
            "grid_auto_flow": null,
            "grid_auto_rows": null,
            "grid_column": null,
            "grid_gap": null,
            "grid_row": null,
            "grid_template_areas": null,
            "grid_template_columns": null,
            "grid_template_rows": null,
            "height": null,
            "justify_content": null,
            "justify_items": null,
            "left": null,
            "margin": null,
            "max_height": null,
            "max_width": null,
            "min_height": null,
            "min_width": null,
            "object_fit": null,
            "object_position": null,
            "order": null,
            "overflow": null,
            "overflow_x": null,
            "overflow_y": null,
            "padding": null,
            "right": null,
            "top": null,
            "visibility": null,
            "width": null
          }
        },
        "f68b63a7346945a5a15a08ee6f45dcf9": {
          "model_module": "@jupyter-widgets/base",
          "model_name": "LayoutModel",
          "model_module_version": "1.2.0",
          "state": {
            "_model_module": "@jupyter-widgets/base",
            "_model_module_version": "1.2.0",
            "_model_name": "LayoutModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "LayoutView",
            "align_content": null,
            "align_items": null,
            "align_self": null,
            "border": null,
            "bottom": null,
            "display": null,
            "flex": null,
            "flex_flow": null,
            "grid_area": null,
            "grid_auto_columns": null,
            "grid_auto_flow": null,
            "grid_auto_rows": null,
            "grid_column": null,
            "grid_gap": null,
            "grid_row": null,
            "grid_template_areas": null,
            "grid_template_columns": null,
            "grid_template_rows": null,
            "height": null,
            "justify_content": null,
            "justify_items": null,
            "left": null,
            "margin": null,
            "max_height": null,
            "max_width": null,
            "min_height": null,
            "min_width": null,
            "object_fit": null,
            "object_position": null,
            "order": null,
            "overflow": null,
            "overflow_x": null,
            "overflow_y": null,
            "padding": null,
            "right": null,
            "top": null,
            "visibility": null,
            "width": null
          }
        },
        "890298022bb644bc9237c8d511023340": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "DescriptionStyleModel",
          "model_module_version": "1.5.0",
          "state": {
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "DescriptionStyleModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "StyleView",
            "description_width": ""
          }
        },
        "aa1b974f234349ee8a6a51455372260f": {
          "model_module": "@jupyter-widgets/base",
          "model_name": "LayoutModel",
          "model_module_version": "1.2.0",
          "state": {
            "_model_module": "@jupyter-widgets/base",
            "_model_module_version": "1.2.0",
            "_model_name": "LayoutModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "LayoutView",
            "align_content": null,
            "align_items": null,
            "align_self": null,
            "border": null,
            "bottom": null,
            "display": null,
            "flex": null,
            "flex_flow": null,
            "grid_area": null,
            "grid_auto_columns": null,
            "grid_auto_flow": null,
            "grid_auto_rows": null,
            "grid_column": null,
            "grid_gap": null,
            "grid_row": null,
            "grid_template_areas": null,
            "grid_template_columns": null,
            "grid_template_rows": null,
            "height": null,
            "justify_content": null,
            "justify_items": null,
            "left": null,
            "margin": null,
            "max_height": null,
            "max_width": null,
            "min_height": null,
            "min_width": null,
            "object_fit": null,
            "object_position": null,
            "order": null,
            "overflow": null,
            "overflow_x": null,
            "overflow_y": null,
            "padding": null,
            "right": null,
            "top": null,
            "visibility": null,
            "width": null
          }
        },
        "7cf0472df1e0401ca206c73f943ad39c": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "ProgressStyleModel",
          "model_module_version": "1.5.0",
          "state": {
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "ProgressStyleModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "StyleView",
            "bar_color": null,
            "description_width": ""
          }
        },
        "80a4bdc04c45489bb31b2f08c0a773a1": {
          "model_module": "@jupyter-widgets/base",
          "model_name": "LayoutModel",
          "model_module_version": "1.2.0",
          "state": {
            "_model_module": "@jupyter-widgets/base",
            "_model_module_version": "1.2.0",
            "_model_name": "LayoutModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "LayoutView",
            "align_content": null,
            "align_items": null,
            "align_self": null,
            "border": null,
            "bottom": null,
            "display": null,
            "flex": null,
            "flex_flow": null,
            "grid_area": null,
            "grid_auto_columns": null,
            "grid_auto_flow": null,
            "grid_auto_rows": null,
            "grid_column": null,
            "grid_gap": null,
            "grid_row": null,
            "grid_template_areas": null,
            "grid_template_columns": null,
            "grid_template_rows": null,
            "height": null,
            "justify_content": null,
            "justify_items": null,
            "left": null,
            "margin": null,
            "max_height": null,
            "max_width": null,
            "min_height": null,
            "min_width": null,
            "object_fit": null,
            "object_position": null,
            "order": null,
            "overflow": null,
            "overflow_x": null,
            "overflow_y": null,
            "padding": null,
            "right": null,
            "top": null,
            "visibility": null,
            "width": null
          }
        },
        "7f59ce2eea2541efbb3dd58049af3eed": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "DescriptionStyleModel",
          "model_module_version": "1.5.0",
          "state": {
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "DescriptionStyleModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "StyleView",
            "description_width": ""
          }
        },
        "0cc06720c44f4f9e93653e74f94bdea5": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "HBoxModel",
          "model_module_version": "1.5.0",
          "state": {
            "_dom_classes": [],
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "HBoxModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/controls",
            "_view_module_version": "1.5.0",
            "_view_name": "HBoxView",
            "box_style": "",
            "children": [
              "IPY_MODEL_553066a3b0a842d382b4201ab01a31ea",
              "IPY_MODEL_82b8247bfcc34da89cbad5c7d369008b",
              "IPY_MODEL_99cdb0a611794e0a88f1b321f0d90ba6"
            ],
            "layout": "IPY_MODEL_1ea7dbd542db43c2a9cc4791e230fe2d"
          }
        },
        "553066a3b0a842d382b4201ab01a31ea": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "HTMLModel",
          "model_module_version": "1.5.0",
          "state": {
            "_dom_classes": [],
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "HTMLModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/controls",
            "_view_module_version": "1.5.0",
            "_view_name": "HTMLView",
            "description": "",
            "description_tooltip": null,
            "layout": "IPY_MODEL_ad09c5bdb7c84ac4971091376493a015",
            "placeholder": "​",
            "style": "IPY_MODEL_0a1e7266f4e44863add8170393923b98",
            "value": "100%"
          }
        },
        "82b8247bfcc34da89cbad5c7d369008b": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "FloatProgressModel",
          "model_module_version": "1.5.0",
          "state": {
            "_dom_classes": [],
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "FloatProgressModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/controls",
            "_view_module_version": "1.5.0",
            "_view_name": "ProgressView",
            "bar_style": "success",
            "description": "",
            "description_tooltip": null,
            "layout": "IPY_MODEL_4c841b365e8b4ec49e1e7420ac42a68e",
            "max": 10000,
            "min": 0,
            "orientation": "horizontal",
            "style": "IPY_MODEL_953cae3598044c56b09d1fceabd2e59e",
            "value": 10000
          }
        },
        "99cdb0a611794e0a88f1b321f0d90ba6": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "HTMLModel",
          "model_module_version": "1.5.0",
          "state": {
            "_dom_classes": [],
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "HTMLModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/controls",
            "_view_module_version": "1.5.0",
            "_view_name": "HTMLView",
            "description": "",
            "description_tooltip": null,
            "layout": "IPY_MODEL_3c5d4eeb526e41e9b964f6ba32dc0997",
            "placeholder": "​",
            "style": "IPY_MODEL_aebe1e5f6f604694bea3df7109b1b16d",
            "value": " 10000/10000 [00:12&lt;00:00, 618.41it/s]"
          }
        },
        "1ea7dbd542db43c2a9cc4791e230fe2d": {
          "model_module": "@jupyter-widgets/base",
          "model_name": "LayoutModel",
          "model_module_version": "1.2.0",
          "state": {
            "_model_module": "@jupyter-widgets/base",
            "_model_module_version": "1.2.0",
            "_model_name": "LayoutModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "LayoutView",
            "align_content": null,
            "align_items": null,
            "align_self": null,
            "border": null,
            "bottom": null,
            "display": null,
            "flex": null,
            "flex_flow": null,
            "grid_area": null,
            "grid_auto_columns": null,
            "grid_auto_flow": null,
            "grid_auto_rows": null,
            "grid_column": null,
            "grid_gap": null,
            "grid_row": null,
            "grid_template_areas": null,
            "grid_template_columns": null,
            "grid_template_rows": null,
            "height": null,
            "justify_content": null,
            "justify_items": null,
            "left": null,
            "margin": null,
            "max_height": null,
            "max_width": null,
            "min_height": null,
            "min_width": null,
            "object_fit": null,
            "object_position": null,
            "order": null,
            "overflow": null,
            "overflow_x": null,
            "overflow_y": null,
            "padding": null,
            "right": null,
            "top": null,
            "visibility": null,
            "width": null
          }
        },
        "ad09c5bdb7c84ac4971091376493a015": {
          "model_module": "@jupyter-widgets/base",
          "model_name": "LayoutModel",
          "model_module_version": "1.2.0",
          "state": {
            "_model_module": "@jupyter-widgets/base",
            "_model_module_version": "1.2.0",
            "_model_name": "LayoutModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "LayoutView",
            "align_content": null,
            "align_items": null,
            "align_self": null,
            "border": null,
            "bottom": null,
            "display": null,
            "flex": null,
            "flex_flow": null,
            "grid_area": null,
            "grid_auto_columns": null,
            "grid_auto_flow": null,
            "grid_auto_rows": null,
            "grid_column": null,
            "grid_gap": null,
            "grid_row": null,
            "grid_template_areas": null,
            "grid_template_columns": null,
            "grid_template_rows": null,
            "height": null,
            "justify_content": null,
            "justify_items": null,
            "left": null,
            "margin": null,
            "max_height": null,
            "max_width": null,
            "min_height": null,
            "min_width": null,
            "object_fit": null,
            "object_position": null,
            "order": null,
            "overflow": null,
            "overflow_x": null,
            "overflow_y": null,
            "padding": null,
            "right": null,
            "top": null,
            "visibility": null,
            "width": null
          }
        },
        "0a1e7266f4e44863add8170393923b98": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "DescriptionStyleModel",
          "model_module_version": "1.5.0",
          "state": {
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "DescriptionStyleModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "StyleView",
            "description_width": ""
          }
        },
        "4c841b365e8b4ec49e1e7420ac42a68e": {
          "model_module": "@jupyter-widgets/base",
          "model_name": "LayoutModel",
          "model_module_version": "1.2.0",
          "state": {
            "_model_module": "@jupyter-widgets/base",
            "_model_module_version": "1.2.0",
            "_model_name": "LayoutModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "LayoutView",
            "align_content": null,
            "align_items": null,
            "align_self": null,
            "border": null,
            "bottom": null,
            "display": null,
            "flex": null,
            "flex_flow": null,
            "grid_area": null,
            "grid_auto_columns": null,
            "grid_auto_flow": null,
            "grid_auto_rows": null,
            "grid_column": null,
            "grid_gap": null,
            "grid_row": null,
            "grid_template_areas": null,
            "grid_template_columns": null,
            "grid_template_rows": null,
            "height": null,
            "justify_content": null,
            "justify_items": null,
            "left": null,
            "margin": null,
            "max_height": null,
            "max_width": null,
            "min_height": null,
            "min_width": null,
            "object_fit": null,
            "object_position": null,
            "order": null,
            "overflow": null,
            "overflow_x": null,
            "overflow_y": null,
            "padding": null,
            "right": null,
            "top": null,
            "visibility": null,
            "width": null
          }
        },
        "953cae3598044c56b09d1fceabd2e59e": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "ProgressStyleModel",
          "model_module_version": "1.5.0",
          "state": {
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "ProgressStyleModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "StyleView",
            "bar_color": null,
            "description_width": ""
          }
        },
        "3c5d4eeb526e41e9b964f6ba32dc0997": {
          "model_module": "@jupyter-widgets/base",
          "model_name": "LayoutModel",
          "model_module_version": "1.2.0",
          "state": {
            "_model_module": "@jupyter-widgets/base",
            "_model_module_version": "1.2.0",
            "_model_name": "LayoutModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "LayoutView",
            "align_content": null,
            "align_items": null,
            "align_self": null,
            "border": null,
            "bottom": null,
            "display": null,
            "flex": null,
            "flex_flow": null,
            "grid_area": null,
            "grid_auto_columns": null,
            "grid_auto_flow": null,
            "grid_auto_rows": null,
            "grid_column": null,
            "grid_gap": null,
            "grid_row": null,
            "grid_template_areas": null,
            "grid_template_columns": null,
            "grid_template_rows": null,
            "height": null,
            "justify_content": null,
            "justify_items": null,
            "left": null,
            "margin": null,
            "max_height": null,
            "max_width": null,
            "min_height": null,
            "min_width": null,
            "object_fit": null,
            "object_position": null,
            "order": null,
            "overflow": null,
            "overflow_x": null,
            "overflow_y": null,
            "padding": null,
            "right": null,
            "top": null,
            "visibility": null,
            "width": null
          }
        },
        "aebe1e5f6f604694bea3df7109b1b16d": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "DescriptionStyleModel",
          "model_module_version": "1.5.0",
          "state": {
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "DescriptionStyleModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "StyleView",
            "description_width": ""
          }
        },
        "4ee7077afe4f43d08d7a6c81c66a6f75": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "HBoxModel",
          "model_module_version": "1.5.0",
          "state": {
            "_dom_classes": [],
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "HBoxModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/controls",
            "_view_module_version": "1.5.0",
            "_view_name": "HBoxView",
            "box_style": "",
            "children": [
              "IPY_MODEL_bb46bf33d2ba4377842e720756eed45d",
              "IPY_MODEL_d133b13a94c94fbe916f94f4bd6addf9",
              "IPY_MODEL_209dce02cf7f4a52b95c1d74f2c550d6"
            ],
            "layout": "IPY_MODEL_e3bc1328aa4d43ada61e96f437999a6a"
          }
        },
        "bb46bf33d2ba4377842e720756eed45d": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "HTMLModel",
          "model_module_version": "1.5.0",
          "state": {
            "_dom_classes": [],
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "HTMLModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/controls",
            "_view_module_version": "1.5.0",
            "_view_name": "HTMLView",
            "description": "",
            "description_tooltip": null,
            "layout": "IPY_MODEL_bf1d9e4e0ac34f6f865942b893734ab1",
            "placeholder": "​",
            "style": "IPY_MODEL_37e3990333024ddaa9163f6aa979d8a7",
            "value": "100%"
          }
        },
        "d133b13a94c94fbe916f94f4bd6addf9": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "FloatProgressModel",
          "model_module_version": "1.5.0",
          "state": {
            "_dom_classes": [],
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "FloatProgressModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/controls",
            "_view_module_version": "1.5.0",
            "_view_name": "ProgressView",
            "bar_style": "success",
            "description": "",
            "description_tooltip": null,
            "layout": "IPY_MODEL_07ea23db87034b1d84cf0a1b3e844268",
            "max": 10000,
            "min": 0,
            "orientation": "horizontal",
            "style": "IPY_MODEL_f84f99ec6d42463a9e0cea727d9d31a3",
            "value": 10000
          }
        },
        "209dce02cf7f4a52b95c1d74f2c550d6": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "HTMLModel",
          "model_module_version": "1.5.0",
          "state": {
            "_dom_classes": [],
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "HTMLModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/controls",
            "_view_module_version": "1.5.0",
            "_view_name": "HTMLView",
            "description": "",
            "description_tooltip": null,
            "layout": "IPY_MODEL_e42dd3b85aaa453a9252fbf4af76f20d",
            "placeholder": "​",
            "style": "IPY_MODEL_4e1e3f0992004ddb94f478da48971554",
            "value": " 10000/10000 [00:18&lt;00:00, 547.46it/s]"
          }
        },
        "e3bc1328aa4d43ada61e96f437999a6a": {
          "model_module": "@jupyter-widgets/base",
          "model_name": "LayoutModel",
          "model_module_version": "1.2.0",
          "state": {
            "_model_module": "@jupyter-widgets/base",
            "_model_module_version": "1.2.0",
            "_model_name": "LayoutModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "LayoutView",
            "align_content": null,
            "align_items": null,
            "align_self": null,
            "border": null,
            "bottom": null,
            "display": null,
            "flex": null,
            "flex_flow": null,
            "grid_area": null,
            "grid_auto_columns": null,
            "grid_auto_flow": null,
            "grid_auto_rows": null,
            "grid_column": null,
            "grid_gap": null,
            "grid_row": null,
            "grid_template_areas": null,
            "grid_template_columns": null,
            "grid_template_rows": null,
            "height": null,
            "justify_content": null,
            "justify_items": null,
            "left": null,
            "margin": null,
            "max_height": null,
            "max_width": null,
            "min_height": null,
            "min_width": null,
            "object_fit": null,
            "object_position": null,
            "order": null,
            "overflow": null,
            "overflow_x": null,
            "overflow_y": null,
            "padding": null,
            "right": null,
            "top": null,
            "visibility": null,
            "width": null
          }
        },
        "bf1d9e4e0ac34f6f865942b893734ab1": {
          "model_module": "@jupyter-widgets/base",
          "model_name": "LayoutModel",
          "model_module_version": "1.2.0",
          "state": {
            "_model_module": "@jupyter-widgets/base",
            "_model_module_version": "1.2.0",
            "_model_name": "LayoutModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "LayoutView",
            "align_content": null,
            "align_items": null,
            "align_self": null,
            "border": null,
            "bottom": null,
            "display": null,
            "flex": null,
            "flex_flow": null,
            "grid_area": null,
            "grid_auto_columns": null,
            "grid_auto_flow": null,
            "grid_auto_rows": null,
            "grid_column": null,
            "grid_gap": null,
            "grid_row": null,
            "grid_template_areas": null,
            "grid_template_columns": null,
            "grid_template_rows": null,
            "height": null,
            "justify_content": null,
            "justify_items": null,
            "left": null,
            "margin": null,
            "max_height": null,
            "max_width": null,
            "min_height": null,
            "min_width": null,
            "object_fit": null,
            "object_position": null,
            "order": null,
            "overflow": null,
            "overflow_x": null,
            "overflow_y": null,
            "padding": null,
            "right": null,
            "top": null,
            "visibility": null,
            "width": null
          }
        },
        "37e3990333024ddaa9163f6aa979d8a7": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "DescriptionStyleModel",
          "model_module_version": "1.5.0",
          "state": {
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "DescriptionStyleModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "StyleView",
            "description_width": ""
          }
        },
        "07ea23db87034b1d84cf0a1b3e844268": {
          "model_module": "@jupyter-widgets/base",
          "model_name": "LayoutModel",
          "model_module_version": "1.2.0",
          "state": {
            "_model_module": "@jupyter-widgets/base",
            "_model_module_version": "1.2.0",
            "_model_name": "LayoutModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "LayoutView",
            "align_content": null,
            "align_items": null,
            "align_self": null,
            "border": null,
            "bottom": null,
            "display": null,
            "flex": null,
            "flex_flow": null,
            "grid_area": null,
            "grid_auto_columns": null,
            "grid_auto_flow": null,
            "grid_auto_rows": null,
            "grid_column": null,
            "grid_gap": null,
            "grid_row": null,
            "grid_template_areas": null,
            "grid_template_columns": null,
            "grid_template_rows": null,
            "height": null,
            "justify_content": null,
            "justify_items": null,
            "left": null,
            "margin": null,
            "max_height": null,
            "max_width": null,
            "min_height": null,
            "min_width": null,
            "object_fit": null,
            "object_position": null,
            "order": null,
            "overflow": null,
            "overflow_x": null,
            "overflow_y": null,
            "padding": null,
            "right": null,
            "top": null,
            "visibility": null,
            "width": null
          }
        },
        "f84f99ec6d42463a9e0cea727d9d31a3": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "ProgressStyleModel",
          "model_module_version": "1.5.0",
          "state": {
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "ProgressStyleModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "StyleView",
            "bar_color": null,
            "description_width": ""
          }
        },
        "e42dd3b85aaa453a9252fbf4af76f20d": {
          "model_module": "@jupyter-widgets/base",
          "model_name": "LayoutModel",
          "model_module_version": "1.2.0",
          "state": {
            "_model_module": "@jupyter-widgets/base",
            "_model_module_version": "1.2.0",
            "_model_name": "LayoutModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "LayoutView",
            "align_content": null,
            "align_items": null,
            "align_self": null,
            "border": null,
            "bottom": null,
            "display": null,
            "flex": null,
            "flex_flow": null,
            "grid_area": null,
            "grid_auto_columns": null,
            "grid_auto_flow": null,
            "grid_auto_rows": null,
            "grid_column": null,
            "grid_gap": null,
            "grid_row": null,
            "grid_template_areas": null,
            "grid_template_columns": null,
            "grid_template_rows": null,
            "height": null,
            "justify_content": null,
            "justify_items": null,
            "left": null,
            "margin": null,
            "max_height": null,
            "max_width": null,
            "min_height": null,
            "min_width": null,
            "object_fit": null,
            "object_position": null,
            "order": null,
            "overflow": null,
            "overflow_x": null,
            "overflow_y": null,
            "padding": null,
            "right": null,
            "top": null,
            "visibility": null,
            "width": null
          }
        },
        "4e1e3f0992004ddb94f478da48971554": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "DescriptionStyleModel",
          "model_module_version": "1.5.0",
          "state": {
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "DescriptionStyleModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "StyleView",
            "description_width": ""
          }
        }
      }
    },
    "accelerator": "GPU"
  },
  "nbformat": 4,
  "nbformat_minor": 5
}