{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "982404fc-3574-4343-8a0a-0017207981e5",
   "metadata": {},
   "source": [
    "## Решение практической задачи 3\n",
    "\n",
    "### Задача: Формирование эффективных рабочих коллективов\n",
    "\n",
    "> Решение практической задачи выполняется в два этапа. На первом этапе необходимо использовать библиотеку OCEAN-AI для получения гипотез предсказаний (оценок персональных качеств личности человека). На втором этапе следует использовать метод _colleague_ranking из библиотеки OCEAN-AI для решения представленной практической задачи на примере поиска подходящих коллег для целевого коллеги. Примеры результатов работы и реализации представлены ниже.\n",
    "\n",
    "> Таким образом, библиотека OCEAN-AI предоставляет инструменты для анализа персональных качеств личности коллег и может помочь в формировании эффективных рабочих групп, улучшении коммуникации и сокращении конфликтов в коллективе.\n",
    "\n",
    "<center>\n",
    "    <img src=\"https://raw.githubusercontent.com/aimclub/OCEANAI/main/docs/source/user_guide/notebooks/images/Step_1_get_scores_en.svg\" width=\"100%\"/>\n",
    "</center>\n",
    "\n",
    "<center>\n",
    "    <img src=\"https://raw.githubusercontent.com/aimclub/OCEANAI/main/docs/source/user_guide/notebooks/images/Step_2_task_3_en.svg\" width=\"100%\"/>\n",
    "</center>\n",
    "\n",
    "<hr>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "68910272-4a5d-415b-8376-41679035dbf4",
   "metadata": {
    "nbsphinx": "hidden",
    "tags": []
   },
   "outputs": [],
   "source": [
    "import os  # Взаимодействие с файловой системой\n",
    "import sys # Доступ к некоторым переменным и функциям Python\n",
    "\n",
    "PATH_TO_SOURCE = os.path.abspath(os.path.dirname(globals()['_dh'][0]))\n",
    "PATH_TO_ROOT = os.path.join(PATH_TO_SOURCE, '..', '..', '..')\n",
    "\n",
    "sys.path.insert(0, os.path.abspath(PATH_TO_ROOT))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "edb8d566-de45-4d82-9b70-ebbb2a3d8213",
   "metadata": {},
   "source": [
    "### `FI V2`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "adb1d9a8-e7e4-4f30-ad1b-272ddc35533a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/markdown": [
       "<span style=\"color:#666\">**[</span><span style=\"color:#1776D2\">2024-10-10 21:44:08</span><span style=\"color:#666\">]</span> <span style=\"color:#666\">Извлечение признаков (экспертных и нейросетевых) из текста ...</span>** "
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "<span style=\"color:#666\">**[</span><span style=\"color:#1776D2\">2024-10-10 21:44:08</span><span style=\"color:#666\">]</span> <span style=\"color:#666\">Получение прогнозов и вычисление точности (мультимодальное объединение) ...</span>**<p><span style=\"color:#666\">&nbsp;&nbsp;&nbsp;&nbsp;10 из 10 (100.0%) ... GitHub\\OCEANAI\\docs\\source\\user_guide\\notebooks\\video_FI\\test\\_plk5k7PBEg.003.mp4 ...</span></p>"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Path</th>\n",
       "      <th>Openness</th>\n",
       "      <th>Conscientiousness</th>\n",
       "      <th>Extraversion</th>\n",
       "      <th>Agreeableness</th>\n",
       "      <th>Non-Neuroticism</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Person ID</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2d6btbaNdfo.000.mp4</td>\n",
       "      <td>0.618917</td>\n",
       "      <td>0.660694</td>\n",
       "      <td>0.477656</td>\n",
       "      <td>0.654437</td>\n",
       "      <td>0.601256</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>300gK3CnzW0.001.mp4</td>\n",
       "      <td>0.461732</td>\n",
       "      <td>0.413451</td>\n",
       "      <td>0.415706</td>\n",
       "      <td>0.498301</td>\n",
       "      <td>0.431224</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>300gK3CnzW0.003.mp4</td>\n",
       "      <td>0.468002</td>\n",
       "      <td>0.448618</td>\n",
       "      <td>0.371742</td>\n",
       "      <td>0.509602</td>\n",
       "      <td>0.453739</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4vdJGgZpj4k.003.mp4</td>\n",
       "      <td>0.585348</td>\n",
       "      <td>0.616446</td>\n",
       "      <td>0.49443</td>\n",
       "      <td>0.605614</td>\n",
       "      <td>0.587017</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>be0DQawtVkE.002.mp4</td>\n",
       "      <td>0.680991</td>\n",
       "      <td>0.56602</td>\n",
       "      <td>0.553915</td>\n",
       "      <td>0.646545</td>\n",
       "      <td>0.64246</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>cLaZxEf1nE4.004.mp4</td>\n",
       "      <td>0.66342</td>\n",
       "      <td>0.551018</td>\n",
       "      <td>0.557912</td>\n",
       "      <td>0.585238</td>\n",
       "      <td>0.587174</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>g24JGYuT74A.004.mp4</td>\n",
       "      <td>0.590237</td>\n",
       "      <td>0.399273</td>\n",
       "      <td>0.409554</td>\n",
       "      <td>0.531861</td>\n",
       "      <td>0.507134</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>JZNMxa3OKHY.000.mp4</td>\n",
       "      <td>0.60577</td>\n",
       "      <td>0.523617</td>\n",
       "      <td>0.531137</td>\n",
       "      <td>0.594406</td>\n",
       "      <td>0.57984</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>nvlqJbHk_Lc.003.mp4</td>\n",
       "      <td>0.511002</td>\n",
       "      <td>0.464702</td>\n",
       "      <td>0.390882</td>\n",
       "      <td>0.443663</td>\n",
       "      <td>0.438811</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>_plk5k7PBEg.003.mp4</td>\n",
       "      <td>0.647606</td>\n",
       "      <td>0.610466</td>\n",
       "      <td>0.524718</td>\n",
       "      <td>0.61428</td>\n",
       "      <td>0.606428</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                          Path  Openness Conscientiousness Extraversion  \\\n",
       "Person ID                                                                 \n",
       "1          2d6btbaNdfo.000.mp4  0.618917          0.660694     0.477656   \n",
       "2          300gK3CnzW0.001.mp4  0.461732          0.413451     0.415706   \n",
       "3          300gK3CnzW0.003.mp4  0.468002          0.448618     0.371742   \n",
       "4          4vdJGgZpj4k.003.mp4  0.585348          0.616446      0.49443   \n",
       "5          be0DQawtVkE.002.mp4  0.680991           0.56602     0.553915   \n",
       "6          cLaZxEf1nE4.004.mp4   0.66342          0.551018     0.557912   \n",
       "7          g24JGYuT74A.004.mp4  0.590237          0.399273     0.409554   \n",
       "8          JZNMxa3OKHY.000.mp4   0.60577          0.523617     0.531137   \n",
       "9          nvlqJbHk_Lc.003.mp4  0.511002          0.464702     0.390882   \n",
       "10         _plk5k7PBEg.003.mp4  0.647606          0.610466     0.524718   \n",
       "\n",
       "          Agreeableness Non-Neuroticism  \n",
       "Person ID                                \n",
       "1              0.654437        0.601256  \n",
       "2              0.498301        0.431224  \n",
       "3              0.509602        0.453739  \n",
       "4              0.605614        0.587017  \n",
       "5              0.646545         0.64246  \n",
       "6              0.585238        0.587174  \n",
       "7              0.531861        0.507134  \n",
       "8              0.594406         0.57984  \n",
       "9              0.443663        0.438811  \n",
       "10              0.61428        0.606428  "
      ]
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    {
     "data": {
      "text/markdown": [
       "<span style=\"color:#666\">**[</span><span style=\"color:#1776D2\">2024-10-10 21:44:08</span><span style=\"color:#666\">]</span> <span style=\"color:#666\">Точность по отдельным персональным качествам личности человека ...</span>** "
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Openness</th>\n",
       "      <th>Conscientiousness</th>\n",
       "      <th>Extraversion</th>\n",
       "      <th>Agreeableness</th>\n",
       "      <th>Non-Neuroticism</th>\n",
       "      <th>Mean</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Metrics</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>MAE</th>\n",
       "      <td>0.0735</td>\n",
       "      <td>0.0631</td>\n",
       "      <td>0.0914</td>\n",
       "      <td>0.0706</td>\n",
       "      <td>0.0691</td>\n",
       "      <td>0.0735</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Accuracy</th>\n",
       "      <td>0.9265</td>\n",
       "      <td>0.9369</td>\n",
       "      <td>0.9086</td>\n",
       "      <td>0.9294</td>\n",
       "      <td>0.9309</td>\n",
       "      <td>0.9265</td>\n",
       "    </tr>\n",
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       "</div>"
      ],
      "text/plain": [
       "          Openness  Conscientiousness  Extraversion  Agreeableness  \\\n",
       "Metrics                                                              \n",
       "MAE         0.0735             0.0631        0.0914         0.0706   \n",
       "Accuracy    0.9265             0.9369        0.9086         0.9294   \n",
       "\n",
       "          Non-Neuroticism    Mean  \n",
       "Metrics                            \n",
       "MAE                0.0691  0.0735  \n",
       "Accuracy           0.9309  0.9265  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "<span style=\"color:#666\">**[</span><span style=\"color:#1776D2\">2024-10-10 21:44:08</span><span style=\"color:#666\">]</span> <span style=\"color:#666\">Средняя средних абсолютных ошибок: <span style=\"color:#1776D2\">0.0735</span>, средняя точность: <span style=\"color:#1776D2\">0.9265</span> ...</span>** "
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "<span style=\"color:#008001\">**Лог файлы успешно сохранены ...**</span>"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "<span style=\"color:#666\">**--- Время выполнения: 35.328 сек. ---**</span>"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Импорт необходимых инструментов\n",
    "import os\n",
    "import pandas as pd\n",
    "\n",
    "# Импорт модуля\n",
    "from oceanai.modules.lab.build import Run\n",
    "\n",
    "# Создание экземпляра класса\n",
    "_b5 = Run()\n",
    "\n",
    "# Настройка ядра\n",
    "_b5.path_to_save_ = './models' # Директория для сохранения файла\n",
    "_b5.chunk_size_ = 2000000      # Размер загрузки файла из сети за 1 шаг\n",
    "\n",
    "corpus = 'fi'\n",
    "\n",
    "# Формирование аудиомоделей\n",
    "res_load_model_hc = _b5.load_audio_model_hc()\n",
    "res_load_model_nn = _b5.load_audio_model_nn()\n",
    "\n",
    "# Загрузка весов аудиомоделей\n",
    "url = _b5.weights_for_big5_['audio'][corpus]['hc']['googledisk']\n",
    "res_load_model_weights_hc = _b5.load_audio_model_weights_hc(url = url, force_reload = False)\n",
    "\n",
    "url = _b5.weights_for_big5_['audio'][corpus]['nn']['googledisk']\n",
    "res_load_model_weights_nn = _b5.load_audio_model_weights_nn(url = url, force_reload = False)\n",
    "\n",
    "# Формирование видеомоделей\n",
    "res_load_model_hc = _b5.load_video_model_hc(lang='en')\n",
    "res_load_model_deep_fe = _b5.load_video_model_deep_fe()\n",
    "res_load_model_nn = _b5.load_video_model_nn()\n",
    "\n",
    "# Загрузка весов видеомоделей\n",
    "url = _b5.weights_for_big5_['video'][corpus]['hc']['googledisk']\n",
    "res_load_model_weights_hc = _b5.load_video_model_weights_hc(url = url, force_reload = False)\n",
    "\n",
    "url = _b5.weights_for_big5_['video'][corpus]['fe']['googledisk']\n",
    "res_load_model_weights_deep_fe = _b5.load_video_model_weights_deep_fe(url = url, force_reload = False)\n",
    "\n",
    "url = _b5.weights_for_big5_['video'][corpus]['nn']['googledisk']\n",
    "res_load_model_weights_nn = _b5.load_video_model_weights_nn(url = url, force_reload = False)\n",
    "\n",
    "# Загрузка словаря с экспертными признаками (текстовая модальность)\n",
    "res_load_text_features = _b5.load_text_features()\n",
    "\n",
    "# Формирование текстовых моделей \n",
    "res_setup_translation_model = _b5.setup_translation_model() # только для русского языка\n",
    "res_setup_translation_model = _b5.setup_bert_encoder(force_reload = False)\n",
    "res_load_text_model_hc_fi = _b5.load_text_model_hc(corpus=corpus)\n",
    "res_load_text_model_nn_fi = _b5.load_text_model_nn(corpus=corpus)\n",
    "\n",
    "# Загрузка весов текстовых моделей\n",
    "url = _b5.weights_for_big5_['text'][corpus]['hc']['googledisk']\n",
    "res_load_text_model_weights_hc_fi = _b5.load_text_model_weights_hc(url = url, force_reload = False)\n",
    "\n",
    "url = _b5.weights_for_big5_['text'][corpus]['nn']['googledisk']\n",
    "res_load_text_model_weights_nn_fi = _b5.load_text_model_weights_nn(url = url, force_reload = False)\n",
    "\n",
    "# Формирование модели для мультимодального объединения информации\n",
    "res_load_avt_model_b5 = _b5.load_avt_model_b5()\n",
    "\n",
    "# Загрузка весов модели для мультимодального объединения информации\n",
    "url = _b5.weights_for_big5_['avt'][corpus]['b5']['googledisk']\n",
    "res_load_avt_model_weights_b5 = _b5.load_avt_model_weights_b5(url = url, force_reload = False)\n",
    "\n",
    "PATH_TO_DIR = './video_FI/'\n",
    "PATH_SAVE_VIDEO = './video_FI/test/'\n",
    "\n",
    "_b5.path_to_save_ = PATH_SAVE_VIDEO\n",
    "\n",
    "# Загрузка 10 тестовых аудиовидеозаписей из корпуса First Impression V2\n",
    "# URL: https://chalearnlap.cvc.uab.cat/dataset/24/description/\n",
    "domain = 'https://download.sberdisk.ru/download/file/'\n",
    "tets_name_files = [\n",
    "    '429713680?token=FqHdMLSSh7zYSZt&filename=_plk5k7PBEg.003.mp4',\n",
    "    '429713681?token=Hz9b4lQkrLfic33&filename=be0DQawtVkE.002.mp4',\n",
    "    '429713683?token=EgUXS9Xs8xHm5gz&filename=2d6btbaNdfo.000.mp4',\n",
    "    '429713684?token=1U26753kmPYdIgt&filename=300gK3CnzW0.003.mp4',\n",
    "    '429713685?token=LyigAWLTzDNwKJO&filename=300gK3CnzW0.001.mp4',\n",
    "    '429713686?token=EpfRbCKHyuc4HPu&filename=cLaZxEf1nE4.004.mp4',\n",
    "    '429713687?token=FNTkwqBr4jOS95l&filename=g24JGYuT74A.004.mp4',\n",
    "    '429713688?token=qDT95nz7hfm2Nki&filename=JZNMxa3OKHY.000.mp4',\n",
    "    '429713689?token=noLguEGXDpbcKhg&filename=nvlqJbHk_Lc.003.mp4',\n",
    "    '429713679?token=9L7RQ0hgdJlcek6&filename=4vdJGgZpj4k.003.mp4'\n",
    "]\n",
    "\n",
    "for curr_files in tets_name_files:\n",
    "    _b5.download_file_from_url(url = domain + curr_files, out = True)\n",
    "\n",
    "# Получение прогнозов\n",
    "_b5.path_to_dataset_ = PATH_TO_DIR # Директория набора данных\n",
    "_b5.ext_ = ['.mp4'] # Расширения искомых файлов\n",
    "\n",
    "# Полный путь к файлу с верными предсказаниями для подсчета точности\n",
    "url_accuracy = _b5.true_traits_[corpus]['googledisk']\n",
    "\n",
    "_b5.get_avt_predictions(url_accuracy = url_accuracy, lang = 'en')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d19fce70-13cb-42ef-9f45-84266b78022e",
   "metadata": {},
   "source": [
    "<hr>\n",
    "\n",
    "Для поиска подходящего коллеги по работе необходимо знать по два коэффициента корреляции для каждого персонального качества личности человека. Эти коэффициенты должны показывать, как изменится оценка качества одного человека, если она будет больше или меньше оценки качества другого человека.\n",
    "\n",
    "В качестве примера предлагается использование коэффициентов корреляции между двумя людьми в контексте отношений \"начальник-подчиненный\", представленных в статье:\n",
    "\n",
    "1) Kuroda S., Yamamoto I. Good boss, bad boss, workers’ mental health and productivity: Evidence from Japan // Japan & The World Economy. – 2018. – vol. 48. – pp. 106-118.\n",
    "\n",
    "Пользователь может установить свои коэффициенты корреляции"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "b5197791-2f26-46e9-9a48-55a22e489827",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Score_comparison</th>\n",
       "      <th>Openness</th>\n",
       "      <th>Conscientiousness</th>\n",
       "      <th>Extraversion</th>\n",
       "      <th>Agreeableness</th>\n",
       "      <th>Non-Neuroticism</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ID</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>higher</td>\n",
       "      <td>-0.0602</td>\n",
       "      <td>0.0471</td>\n",
       "      <td>-0.1070</td>\n",
       "      <td>-0.0832</td>\n",
       "      <td>0.190</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>lower</td>\n",
       "      <td>-0.1720</td>\n",
       "      <td>-0.1050</td>\n",
       "      <td>0.0772</td>\n",
       "      <td>0.0703</td>\n",
       "      <td>-0.229</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Score_comparison  Openness  Conscientiousness  Extraversion  Agreeableness  \\\n",
       "ID                                                                              \n",
       "1            higher   -0.0602             0.0471       -0.1070        -0.0832   \n",
       "2             lower   -0.1720            -0.1050        0.0772         0.0703   \n",
       "\n",
       "    Non-Neuroticism  \n",
       "ID                   \n",
       "1             0.190  \n",
       "2            -0.229  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Загрузка датафрейма с коэффициентами корреляции\n",
    "url = 'https://download.sberdisk.ru/download/file/478675819?token=LuB7L1QsEY0UuSs&filename=colleague_ranking.csv'\n",
    "df_correlation_coefficients = pd.read_csv(url)\n",
    "df_correlation_coefficients = pd.DataFrame(\n",
    "    df_correlation_coefficients.drop(['ID'], axis = 1)\n",
    ")\n",
    "\n",
    "df_correlation_coefficients.index.name = 'ID'\n",
    "df_correlation_coefficients.index += 1\n",
    "df_correlation_coefficients.index = df_correlation_coefficients.index.map(str)\n",
    "\n",
    "df_correlation_coefficients"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f9de212b-ffa0-4610-a8c3-4f698e0e379c",
   "metadata": {},
   "source": [
    "#### Поиск старшего коллеги"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "34257b78-d910-4a7e-b89b-cb118f55543d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Path</th>\n",
       "      <th>OPE</th>\n",
       "      <th>CON</th>\n",
       "      <th>EXT</th>\n",
       "      <th>AGR</th>\n",
       "      <th>NNEU</th>\n",
       "      <th>Match</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Person ID</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>300gK3CnzW0.003.mp4</td>\n",
       "      <td>0.468</td>\n",
       "      <td>0.449</td>\n",
       "      <td>0.372</td>\n",
       "      <td>0.510</td>\n",
       "      <td>0.454</td>\n",
       "      <td>0.023</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>g24JGYuT74A.004.mp4</td>\n",
       "      <td>0.590</td>\n",
       "      <td>0.399</td>\n",
       "      <td>0.410</td>\n",
       "      <td>0.532</td>\n",
       "      <td>0.507</td>\n",
       "      <td>0.006</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2d6btbaNdfo.000.mp4</td>\n",
       "      <td>0.619</td>\n",
       "      <td>0.661</td>\n",
       "      <td>0.478</td>\n",
       "      <td>0.654</td>\n",
       "      <td>0.601</td>\n",
       "      <td>0.002</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4vdJGgZpj4k.003.mp4</td>\n",
       "      <td>0.585</td>\n",
       "      <td>0.616</td>\n",
       "      <td>0.494</td>\n",
       "      <td>0.606</td>\n",
       "      <td>0.587</td>\n",
       "      <td>0.002</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>_plk5k7PBEg.003.mp4</td>\n",
       "      <td>0.648</td>\n",
       "      <td>0.610</td>\n",
       "      <td>0.525</td>\n",
       "      <td>0.614</td>\n",
       "      <td>0.606</td>\n",
       "      <td>-0.002</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>be0DQawtVkE.002.mp4</td>\n",
       "      <td>0.681</td>\n",
       "      <td>0.566</td>\n",
       "      <td>0.554</td>\n",
       "      <td>0.647</td>\n",
       "      <td>0.642</td>\n",
       "      <td>-0.005</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>JZNMxa3OKHY.000.mp4</td>\n",
       "      <td>0.606</td>\n",
       "      <td>0.524</td>\n",
       "      <td>0.531</td>\n",
       "      <td>0.594</td>\n",
       "      <td>0.580</td>\n",
       "      <td>-0.008</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>cLaZxEf1nE4.004.mp4</td>\n",
       "      <td>0.663</td>\n",
       "      <td>0.551</td>\n",
       "      <td>0.558</td>\n",
       "      <td>0.585</td>\n",
       "      <td>0.587</td>\n",
       "      <td>-0.011</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>300gK3CnzW0.001.mp4</td>\n",
       "      <td>0.462</td>\n",
       "      <td>0.413</td>\n",
       "      <td>0.416</td>\n",
       "      <td>0.498</td>\n",
       "      <td>0.431</td>\n",
       "      <td>-0.154</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>nvlqJbHk_Lc.003.mp4</td>\n",
       "      <td>0.511</td>\n",
       "      <td>0.465</td>\n",
       "      <td>0.391</td>\n",
       "      <td>0.444</td>\n",
       "      <td>0.439</td>\n",
       "      <td>-0.176</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                          Path    OPE    CON    EXT    AGR   NNEU  Match\n",
       "Person ID                                                               \n",
       "3          300gK3CnzW0.003.mp4  0.468  0.449  0.372  0.510  0.454  0.023\n",
       "7          g24JGYuT74A.004.mp4  0.590  0.399  0.410  0.532  0.507  0.006\n",
       "1          2d6btbaNdfo.000.mp4  0.619  0.661  0.478  0.654  0.601  0.002\n",
       "4          4vdJGgZpj4k.003.mp4  0.585  0.616  0.494  0.606  0.587  0.002\n",
       "10         _plk5k7PBEg.003.mp4  0.648  0.610  0.525  0.614  0.606 -0.002\n",
       "5          be0DQawtVkE.002.mp4  0.681  0.566  0.554  0.647  0.642 -0.005\n",
       "8          JZNMxa3OKHY.000.mp4  0.606  0.524  0.531  0.594  0.580 -0.008\n",
       "6          cLaZxEf1nE4.004.mp4  0.663  0.551  0.558  0.585  0.587 -0.011\n",
       "2          300gK3CnzW0.001.mp4  0.462  0.413  0.416  0.498  0.431 -0.154\n",
       "9          nvlqJbHk_Lc.003.mp4  0.511  0.465  0.391  0.444  0.439 -0.176"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Список оценок персональных качеств личности целевого человека\n",
    "target_scores = [0.527886, 0.522337, 0.458468, 0.51761, 0.444649]\n",
    "\n",
    "_b5._colleague_ranking(\n",
    "    correlation_coefficients = df_correlation_coefficients,\n",
    "    target_scores = target_scores,\n",
    "    colleague = 'major',\n",
    "    equal_coefficients = 0.5,\n",
    "    out = False\n",
    ")\n",
    "\n",
    "_b5._save_logs(df = _b5.df_files_colleague_, name = 'major_colleague_ranking_fi_en', out = True)\n",
    "\n",
    "# Опционно\n",
    "df = _b5.df_files_colleague_.rename(columns = {'Openness':'OPE', 'Conscientiousness':'CON', 'Extraversion': 'EXT', 'Agreeableness': 'AGR', 'Non-Neuroticism': 'NNEU'})\n",
    "columns_to_round = df.columns[1:]\n",
    "df[columns_to_round] = df[columns_to_round].apply(lambda x: [round(i, 3) for i in x])\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ff96e61d-c1b7-4a8e-b437-a9d368c613ec",
   "metadata": {},
   "source": [
    "#### Поиск младшего коллеги"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "7c5eefd9-f908-4e28-b93d-a8c8076125f7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Path</th>\n",
       "      <th>OPE</th>\n",
       "      <th>CON</th>\n",
       "      <th>EXT</th>\n",
       "      <th>AGR</th>\n",
       "      <th>NNEU</th>\n",
       "      <th>Match</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Person ID</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>nvlqJbHk_Lc.003.mp4</td>\n",
       "      <td>0.511</td>\n",
       "      <td>0.465</td>\n",
       "      <td>0.391</td>\n",
       "      <td>0.444</td>\n",
       "      <td>0.439</td>\n",
       "      <td>-0.004</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>300gK3CnzW0.001.mp4</td>\n",
       "      <td>0.462</td>\n",
       "      <td>0.413</td>\n",
       "      <td>0.416</td>\n",
       "      <td>0.498</td>\n",
       "      <td>0.431</td>\n",
       "      <td>-0.012</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>300gK3CnzW0.003.mp4</td>\n",
       "      <td>0.468</td>\n",
       "      <td>0.449</td>\n",
       "      <td>0.372</td>\n",
       "      <td>0.510</td>\n",
       "      <td>0.454</td>\n",
       "      <td>-0.193</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>g24JGYuT74A.004.mp4</td>\n",
       "      <td>0.590</td>\n",
       "      <td>0.399</td>\n",
       "      <td>0.410</td>\n",
       "      <td>0.532</td>\n",
       "      <td>0.507</td>\n",
       "      <td>-0.205</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>JZNMxa3OKHY.000.mp4</td>\n",
       "      <td>0.606</td>\n",
       "      <td>0.524</td>\n",
       "      <td>0.531</td>\n",
       "      <td>0.594</td>\n",
       "      <td>0.580</td>\n",
       "      <td>-0.209</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4vdJGgZpj4k.003.mp4</td>\n",
       "      <td>0.585</td>\n",
       "      <td>0.616</td>\n",
       "      <td>0.494</td>\n",
       "      <td>0.606</td>\n",
       "      <td>0.587</td>\n",
       "      <td>-0.219</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>cLaZxEf1nE4.004.mp4</td>\n",
       "      <td>0.663</td>\n",
       "      <td>0.551</td>\n",
       "      <td>0.558</td>\n",
       "      <td>0.585</td>\n",
       "      <td>0.587</td>\n",
       "      <td>-0.222</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>_plk5k7PBEg.003.mp4</td>\n",
       "      <td>0.648</td>\n",
       "      <td>0.610</td>\n",
       "      <td>0.525</td>\n",
       "      <td>0.614</td>\n",
       "      <td>0.606</td>\n",
       "      <td>-0.231</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2d6btbaNdfo.000.mp4</td>\n",
       "      <td>0.619</td>\n",
       "      <td>0.661</td>\n",
       "      <td>0.478</td>\n",
       "      <td>0.654</td>\n",
       "      <td>0.601</td>\n",
       "      <td>-0.231</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>be0DQawtVkE.002.mp4</td>\n",
       "      <td>0.681</td>\n",
       "      <td>0.566</td>\n",
       "      <td>0.554</td>\n",
       "      <td>0.647</td>\n",
       "      <td>0.642</td>\n",
       "      <td>-0.236</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                          Path    OPE    CON    EXT    AGR   NNEU  Match\n",
       "Person ID                                                               \n",
       "9          nvlqJbHk_Lc.003.mp4  0.511  0.465  0.391  0.444  0.439 -0.004\n",
       "2          300gK3CnzW0.001.mp4  0.462  0.413  0.416  0.498  0.431 -0.012\n",
       "3          300gK3CnzW0.003.mp4  0.468  0.449  0.372  0.510  0.454 -0.193\n",
       "7          g24JGYuT74A.004.mp4  0.590  0.399  0.410  0.532  0.507 -0.205\n",
       "8          JZNMxa3OKHY.000.mp4  0.606  0.524  0.531  0.594  0.580 -0.209\n",
       "4          4vdJGgZpj4k.003.mp4  0.585  0.616  0.494  0.606  0.587 -0.219\n",
       "6          cLaZxEf1nE4.004.mp4  0.663  0.551  0.558  0.585  0.587 -0.222\n",
       "10         _plk5k7PBEg.003.mp4  0.648  0.610  0.525  0.614  0.606 -0.231\n",
       "1          2d6btbaNdfo.000.mp4  0.619  0.661  0.478  0.654  0.601 -0.231\n",
       "5          be0DQawtVkE.002.mp4  0.681  0.566  0.554  0.647  0.642 -0.236"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Список оценок персональных качеств личности целевого человека\n",
    "target_scores = [0.527886, 0.522337, 0.458468, 0.51761, 0.444649]\n",
    "\n",
    "_b5._colleague_ranking(\n",
    "    correlation_coefficients = df_correlation_coefficients,\n",
    "    target_scores = target_scores,\n",
    "    colleague = 'minor',\n",
    "    equal_coefficients = 0.5,\n",
    "    out = False\n",
    ")\n",
    "\n",
    "_b5._save_logs(df = _b5.df_files_colleague_, name = 'minor_colleague_ranking_fi_en', out = True)\n",
    "\n",
    "# Опционно\n",
    "df = _b5.df_files_colleague_.rename(columns = {'Openness':'OPE', 'Conscientiousness':'CON', 'Extraversion': 'EXT', 'Agreeableness': 'AGR', 'Non-Neuroticism': 'NNEU'})\n",
    "columns_to_round = df.columns[1:]\n",
    "df[columns_to_round] = df[columns_to_round].apply(lambda x: [round(i, 3) for i in x])\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7f218cd2",
   "metadata": {},
   "source": [
    "<hr>\n",
    "\n",
    "Для поиска подходящего коллеги по типу личности MBTI необходимо знать коэффициенты корреляции между личностными качествами человека и типами личности MBTI, а также оценки этих качеств для целевого человека.\n",
    "\n",
    "В качестве примера предлагается использование коэффициентов корреляции, представленных в статье:\n",
    "\n",
    "1) Furnham A. The big five facets and the MBTI: The relationship between the 30 NEO-PI (R) Facets and the four Myers-Briggs Type Indicator (MBTI) scores // Psychology. - 2022. vol. 13(10). - pp. 1504-1516.\n",
    "\n",
    "Пользователь может установить свои коэффициенты корреляции"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "76203e38",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Trait</th>\n",
       "      <th>EI</th>\n",
       "      <th>SN</th>\n",
       "      <th>TF</th>\n",
       "      <th>JP</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ID</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Openness</td>\n",
       "      <td>0.09</td>\n",
       "      <td>-0.03</td>\n",
       "      <td>-0.14</td>\n",
       "      <td>-0.16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Conscientiousness</td>\n",
       "      <td>0.04</td>\n",
       "      <td>-0.04</td>\n",
       "      <td>0.20</td>\n",
       "      <td>0.14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Extraversion</td>\n",
       "      <td>0.20</td>\n",
       "      <td>-0.03</td>\n",
       "      <td>0.01</td>\n",
       "      <td>-0.07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Agreeableness</td>\n",
       "      <td>0.02</td>\n",
       "      <td>0.05</td>\n",
       "      <td>-0.35</td>\n",
       "      <td>0.03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Non-Neuroticism</td>\n",
       "      <td>0.08</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.16</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                Trait    EI    SN    TF    JP\n",
       "ID                                           \n",
       "1            Openness  0.09 -0.03 -0.14 -0.16\n",
       "2   Conscientiousness  0.04 -0.04  0.20  0.14\n",
       "3        Extraversion  0.20 -0.03  0.01 -0.07\n",
       "4       Agreeableness  0.02  0.05 -0.35  0.03\n",
       "5     Non-Neuroticism  0.08  0.00  0.16  0.00"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Загрузка датафрейма с коэффициентами корреляции\n",
    "url = 'https://download.sberdisk.ru/download/file/493644095?token=EX7hFxNJhMoLumI&filename=df_mbti_correlation.csv'\n",
    "df_correlation_coefficients = pd.read_csv(url)\n",
    "\n",
    "df_correlation_coefficients.index.name = 'ID'\n",
    "df_correlation_coefficients.index += 1\n",
    "df_correlation_coefficients.index = df_correlation_coefficients.index.map(str)\n",
    "\n",
    "df_correlation_coefficients"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "ddd7fa82",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Path</th>\n",
       "      <th>OPE</th>\n",
       "      <th>CON</th>\n",
       "      <th>EXT</th>\n",
       "      <th>AGR</th>\n",
       "      <th>NNEU</th>\n",
       "      <th>EI</th>\n",
       "      <th>SN</th>\n",
       "      <th>TF</th>\n",
       "      <th>JP</th>\n",
       "      <th>MBTI</th>\n",
       "      <th>Match</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Person ID</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>300gK3CnzW0.001.mp4</td>\n",
       "      <td>0.462</td>\n",
       "      <td>0.413</td>\n",
       "      <td>0.416</td>\n",
       "      <td>0.498</td>\n",
       "      <td>0.431</td>\n",
       "      <td>-0.185699</td>\n",
       "      <td>0.017946</td>\n",
       "      <td>0.083205</td>\n",
       "      <td>0.030144</td>\n",
       "      <td>ISTJ</td>\n",
       "      <td>100.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>300gK3CnzW0.003.mp4</td>\n",
       "      <td>0.468</td>\n",
       "      <td>0.449</td>\n",
       "      <td>0.372</td>\n",
       "      <td>0.510</td>\n",
       "      <td>0.454</td>\n",
       "      <td>-0.160520</td>\n",
       "      <td>0.068617</td>\n",
       "      <td>-0.278880</td>\n",
       "      <td>0.053384</td>\n",
       "      <td>ISFJ</td>\n",
       "      <td>75.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2d6btbaNdfo.000.mp4</td>\n",
       "      <td>0.619</td>\n",
       "      <td>0.661</td>\n",
       "      <td>0.478</td>\n",
       "      <td>0.654</td>\n",
       "      <td>0.601</td>\n",
       "      <td>0.047788</td>\n",
       "      <td>0.002056</td>\n",
       "      <td>-0.092138</td>\n",
       "      <td>0.046539</td>\n",
       "      <td>ESFJ</td>\n",
       "      <td>50.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4vdJGgZpj4k.003.mp4</td>\n",
       "      <td>0.585</td>\n",
       "      <td>0.616</td>\n",
       "      <td>0.494</td>\n",
       "      <td>0.606</td>\n",
       "      <td>0.587</td>\n",
       "      <td>0.037527</td>\n",
       "      <td>0.002895</td>\n",
       "      <td>-0.081646</td>\n",
       "      <td>0.045425</td>\n",
       "      <td>ESFJ</td>\n",
       "      <td>50.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>g24JGYuT74A.004.mp4</td>\n",
       "      <td>0.590</td>\n",
       "      <td>0.399</td>\n",
       "      <td>0.410</td>\n",
       "      <td>0.532</td>\n",
       "      <td>0.507</td>\n",
       "      <td>0.006447</td>\n",
       "      <td>0.037143</td>\n",
       "      <td>-0.271593</td>\n",
       "      <td>-0.105712</td>\n",
       "      <td>ESFP</td>\n",
       "      <td>25.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>nvlqJbHk_Lc.003.mp4</td>\n",
       "      <td>0.511</td>\n",
       "      <td>0.465</td>\n",
       "      <td>0.391</td>\n",
       "      <td>0.444</td>\n",
       "      <td>0.439</td>\n",
       "      <td>-0.094752</td>\n",
       "      <td>-0.007199</td>\n",
       "      <td>-0.083317</td>\n",
       "      <td>-0.132767</td>\n",
       "      <td>INFP</td>\n",
       "      <td>25.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>be0DQawtVkE.002.mp4</td>\n",
       "      <td>0.681</td>\n",
       "      <td>0.566</td>\n",
       "      <td>0.554</td>\n",
       "      <td>0.647</td>\n",
       "      <td>0.642</td>\n",
       "      <td>0.259041</td>\n",
       "      <td>-0.027361</td>\n",
       "      <td>-0.100093</td>\n",
       "      <td>-0.049093</td>\n",
       "      <td>ENFP</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>cLaZxEf1nE4.004.mp4</td>\n",
       "      <td>0.663</td>\n",
       "      <td>0.551</td>\n",
       "      <td>0.558</td>\n",
       "      <td>0.585</td>\n",
       "      <td>0.587</td>\n",
       "      <td>0.252010</td>\n",
       "      <td>-0.029419</td>\n",
       "      <td>-0.087981</td>\n",
       "      <td>-0.050501</td>\n",
       "      <td>ENFP</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>JZNMxa3OKHY.000.mp4</td>\n",
       "      <td>0.606</td>\n",
       "      <td>0.524</td>\n",
       "      <td>0.531</td>\n",
       "      <td>0.594</td>\n",
       "      <td>0.580</td>\n",
       "      <td>0.239967</td>\n",
       "      <td>-0.025332</td>\n",
       "      <td>-0.090041</td>\n",
       "      <td>-0.042964</td>\n",
       "      <td>ENFP</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>_plk5k7PBEg.003.mp4</td>\n",
       "      <td>0.648</td>\n",
       "      <td>0.610</td>\n",
       "      <td>0.525</td>\n",
       "      <td>0.614</td>\n",
       "      <td>0.606</td>\n",
       "      <td>0.248447</td>\n",
       "      <td>-0.028874</td>\n",
       "      <td>-0.081294</td>\n",
       "      <td>-0.036454</td>\n",
       "      <td>ENFP</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                          Path    OPE    CON    EXT    AGR   NNEU        EI  \\\n",
       "Person ID                                                                     \n",
       "2          300gK3CnzW0.001.mp4  0.462  0.413  0.416  0.498  0.431 -0.185699   \n",
       "3          300gK3CnzW0.003.mp4  0.468  0.449  0.372  0.510  0.454 -0.160520   \n",
       "1          2d6btbaNdfo.000.mp4  0.619  0.661  0.478  0.654  0.601  0.047788   \n",
       "4          4vdJGgZpj4k.003.mp4  0.585  0.616  0.494  0.606  0.587  0.037527   \n",
       "7          g24JGYuT74A.004.mp4  0.590  0.399  0.410  0.532  0.507  0.006447   \n",
       "9          nvlqJbHk_Lc.003.mp4  0.511  0.465  0.391  0.444  0.439 -0.094752   \n",
       "5          be0DQawtVkE.002.mp4  0.681  0.566  0.554  0.647  0.642  0.259041   \n",
       "6          cLaZxEf1nE4.004.mp4  0.663  0.551  0.558  0.585  0.587  0.252010   \n",
       "8          JZNMxa3OKHY.000.mp4  0.606  0.524  0.531  0.594  0.580  0.239967   \n",
       "10         _plk5k7PBEg.003.mp4  0.648  0.610  0.525  0.614  0.606  0.248447   \n",
       "\n",
       "                 SN        TF        JP  MBTI  Match  \n",
       "Person ID                                             \n",
       "2          0.017946  0.083205  0.030144  ISTJ  100.0  \n",
       "3          0.068617 -0.278880  0.053384  ISFJ   75.0  \n",
       "1          0.002056 -0.092138  0.046539  ESFJ   50.0  \n",
       "4          0.002895 -0.081646  0.045425  ESFJ   50.0  \n",
       "7          0.037143 -0.271593 -0.105712  ESFP   25.0  \n",
       "9         -0.007199 -0.083317 -0.132767  INFP   25.0  \n",
       "5         -0.027361 -0.100093 -0.049093  ENFP    0.0  \n",
       "6         -0.029419 -0.087981 -0.050501  ENFP    0.0  \n",
       "8         -0.025332 -0.090041 -0.042964  ENFP    0.0  \n",
       "10        -0.028874 -0.081294 -0.036454  ENFP    0.0  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "_b5._colleague_personality_type_match(\n",
    "    correlation_coefficients = df_correlation_coefficients,\n",
    "    target_scores = [0.34, 0.56, 0.42, 0.57, 0.56],\n",
    "    threshold = 0.5,\n",
    "    out = True\n",
    ")\n",
    "\n",
    "_b5._save_logs(df = _b5.df_files_MBTI_colleague_match_, name = 'MBTI_colleague_personality_type_match_en', out = True)\n",
    "\n",
    "# Optional\n",
    "df = _b5.df_files_MBTI_colleague_match_.rename(columns = {'Openness':'OPE', 'Conscientiousness':'CON', 'Extraversion': 'EXT', 'Agreeableness': 'AGR', 'Non-Neuroticism': 'NNEU'})\n",
    "columns_to_round = df.columns[1:6]\n",
    "df[columns_to_round] = df[columns_to_round].apply(lambda x: [round(i, 3) for i in x])\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bbbb9737",
   "metadata": {},
   "source": [
    "<hr>\n",
    "\n",
    "Для определения степени выраженности персональных растройств необходимо знать коэффициенты корреляции между персональными качествами личности человека и типами личности MBTI, а также кооэффициенты корреляции между типами личности MBTI и персональными растройствами.\n",
    "\n",
    "В качестве примера предлагается использование коэффициентов корреляции между персональными качествами личности человека и типами личности MBTI, представленных в статье [1] и кооэффициентов корреляции между типами личности MBTI и персональными растройствами [2].\n",
    "\n",
    "1) Furnham A. The big five facets and the MBTI: The relationship between the 30 NEO-PI (R) Facets and the four Myers-Briggs Type Indicator (MBTI) scores // Psychology. - 2022. vol. 13(10). - pp. 1504-1516.\n",
    "2) Furnham A. MBTI and aberrant personality traits: dark-side trait correlates of the Myers Briggs type inventory // Psychology. - 2022. - vol. 13(5). - pp 805-815.\n",
    "\n",
    "Среди персональных расстройста рассматриваются следующие:\n",
    "1) Параноидное (Paranoid) — Недоверие и подозрительность по отношению к другим; мотивы интерпретируются как злонамеренные.\n",
    "2) Шизоидное (Schizoid) — Эмоциональная холодность и отстранённость от социальных отношений; безразличие к похвалам и критике.\n",
    "3) Шизотипическое (Schizotypal) — Странные убеждения или магическое мышление; поведение или речь, которые кажутся странными, эксцентричными или необычными.\n",
    "4) Антисоциальное (Antisocial) — Пренебрежение к истине; импульсивность и неспособность планировать будущее; нарушение социальных норм.\n",
    "5) Пограничное (Borderline) — Неуместная злость; нестабильные и интенсивные отношения, которые чередуются между идеализацией и обесцениванием.\n",
    "6) Истерическое (Histrionic) — Чрезмерная эмоциональность и стремление к вниманию; драматизация, театральное поведение и преувеличенное выражение эмоций.\n",
    "7) Нарциссическое (Narcissistic) — Высокомерные и надменные манеры или установки, преувеличенное чувство собственной значимости и права на особое отношение.\n",
    "8) Избегающее (Avoidant) — Социальное избегание; чувство неполноценности и повышенная чувствительность к критике или отказу.\n",
    "9) Зависимое (Dependent) — Трудности в принятии повседневных решений без чрезмерных советов и уверений; трудности в выражении несогласия из-за страха потери поддержки или одобрения.\n",
    "10) Обсессивно-компульсивное личностное расстройство (OCPD) — Чрезмерная озабоченность порядком, перфекционизмом, контролем и деталями; стремление к соблюдению правил, часто в ущерб гибкости и эффективности.\n",
    "\n",
    "\n",
    "Пользователь может установить свои коэффициенты корреляции"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "2ef97e79",
   "metadata": {},
   "outputs": [],
   "source": [
    "url = 'https://download.sberdisk.ru/download/file/493644095?token=EX7hFxNJhMoLumI&filename=df_mbti_correlation.csv'\n",
    "df_correlation_coefficients_mbti = pd.read_csv(url)\n",
    "\n",
    "df_correlation_coefficients_mbti.index.name = 'ID'\n",
    "df_correlation_coefficients_mbti.index += 1\n",
    "df_correlation_coefficients_mbti.index = df_correlation_coefficients_mbti.index.map(str)\n",
    "\n",
    "url = 'https://download.sberdisk.ru/download/file/493644096?token=T309xfzRosPj3v9&filename=df_disorder_correlation.csv'\n",
    "df_correlation_coefficients_disorders = pd.read_csv(url)\n",
    "\n",
    "df_correlation_coefficients_disorders.index.name = 'ID'\n",
    "df_correlation_coefficients_disorders.index += 1\n",
    "df_correlation_coefficients_disorders.index = df_correlation_coefficients_disorders.index.map(str)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "05fe491a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Path</th>\n",
       "      <th>OPE</th>\n",
       "      <th>CON</th>\n",
       "      <th>EXT</th>\n",
       "      <th>AGR</th>\n",
       "      <th>NNEU</th>\n",
       "      <th>MBTI</th>\n",
       "      <th>Disorder 1</th>\n",
       "      <th>Disorder 2</th>\n",
       "      <th>Disorder 3</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Person ID</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2d6btbaNdfo.000.mp4</td>\n",
       "      <td>0.619</td>\n",
       "      <td>0.661</td>\n",
       "      <td>0.478</td>\n",
       "      <td>0.654</td>\n",
       "      <td>0.601</td>\n",
       "      <td>ESFJ</td>\n",
       "      <td>Narcissistic (0.034)</td>\n",
       "      <td>Paranoid (0.033)</td>\n",
       "      <td>Dependent (0.028)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>300gK3CnzW0.001.mp4</td>\n",
       "      <td>0.462</td>\n",
       "      <td>0.413</td>\n",
       "      <td>0.416</td>\n",
       "      <td>0.498</td>\n",
       "      <td>0.431</td>\n",
       "      <td>ISTJ</td>\n",
       "      <td>Schizoid (0.039)</td>\n",
       "      <td>Avoidant (0.022)</td>\n",
       "      <td>Dependent (0.012)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>300gK3CnzW0.003.mp4</td>\n",
       "      <td>0.468</td>\n",
       "      <td>0.449</td>\n",
       "      <td>0.372</td>\n",
       "      <td>0.510</td>\n",
       "      <td>0.454</td>\n",
       "      <td>ISFJ</td>\n",
       "      <td>Dependent (0.089)</td>\n",
       "      <td>Narcissistic (0.087)</td>\n",
       "      <td>Paranoid (0.082)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4vdJGgZpj4k.003.mp4</td>\n",
       "      <td>0.585</td>\n",
       "      <td>0.616</td>\n",
       "      <td>0.494</td>\n",
       "      <td>0.606</td>\n",
       "      <td>0.587</td>\n",
       "      <td>ESFJ</td>\n",
       "      <td>Narcissistic (0.03)</td>\n",
       "      <td>Paranoid (0.029)</td>\n",
       "      <td>Dependent (0.025)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>be0DQawtVkE.002.mp4</td>\n",
       "      <td>0.681</td>\n",
       "      <td>0.566</td>\n",
       "      <td>0.554</td>\n",
       "      <td>0.647</td>\n",
       "      <td>0.642</td>\n",
       "      <td>ENFP</td>\n",
       "      <td>Paranoid (0.067)</td>\n",
       "      <td>Narcissistic (0.064)</td>\n",
       "      <td>Histrionic (0.062)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>cLaZxEf1nE4.004.mp4</td>\n",
       "      <td>0.663</td>\n",
       "      <td>0.551</td>\n",
       "      <td>0.558</td>\n",
       "      <td>0.585</td>\n",
       "      <td>0.587</td>\n",
       "      <td>ENFP</td>\n",
       "      <td>Paranoid (0.063)</td>\n",
       "      <td>Histrionic (0.06)</td>\n",
       "      <td>Narcissistic (0.06)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>g24JGYuT74A.004.mp4</td>\n",
       "      <td>0.590</td>\n",
       "      <td>0.399</td>\n",
       "      <td>0.410</td>\n",
       "      <td>0.532</td>\n",
       "      <td>0.507</td>\n",
       "      <td>ESFP</td>\n",
       "      <td>Narcissistic (0.09)</td>\n",
       "      <td>Paranoid (0.085)</td>\n",
       "      <td>Dependent (0.074)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>JZNMxa3OKHY.000.mp4</td>\n",
       "      <td>0.606</td>\n",
       "      <td>0.524</td>\n",
       "      <td>0.531</td>\n",
       "      <td>0.594</td>\n",
       "      <td>0.580</td>\n",
       "      <td>ENFP</td>\n",
       "      <td>Paranoid (0.061)</td>\n",
       "      <td>Narcissistic (0.058)</td>\n",
       "      <td>Histrionic (0.057)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>nvlqJbHk_Lc.003.mp4</td>\n",
       "      <td>0.511</td>\n",
       "      <td>0.465</td>\n",
       "      <td>0.391</td>\n",
       "      <td>0.444</td>\n",
       "      <td>0.439</td>\n",
       "      <td>INFP</td>\n",
       "      <td>Avoidant (0.036)</td>\n",
       "      <td>Schizoid (0.035)</td>\n",
       "      <td>Narcissistic (0.031)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>_plk5k7PBEg.003.mp4</td>\n",
       "      <td>0.648</td>\n",
       "      <td>0.610</td>\n",
       "      <td>0.525</td>\n",
       "      <td>0.614</td>\n",
       "      <td>0.606</td>\n",
       "      <td>ENFP</td>\n",
       "      <td>Paranoid (0.06)</td>\n",
       "      <td>Histrionic (0.057)</td>\n",
       "      <td>Narcissistic (0.056)</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                          Path    OPE    CON    EXT    AGR   NNEU  MBTI  \\\n",
       "Person ID                                                                 \n",
       "1          2d6btbaNdfo.000.mp4  0.619  0.661  0.478  0.654  0.601  ESFJ   \n",
       "2          300gK3CnzW0.001.mp4  0.462  0.413  0.416  0.498  0.431  ISTJ   \n",
       "3          300gK3CnzW0.003.mp4  0.468  0.449  0.372  0.510  0.454  ISFJ   \n",
       "4          4vdJGgZpj4k.003.mp4  0.585  0.616  0.494  0.606  0.587  ESFJ   \n",
       "5          be0DQawtVkE.002.mp4  0.681  0.566  0.554  0.647  0.642  ENFP   \n",
       "6          cLaZxEf1nE4.004.mp4  0.663  0.551  0.558  0.585  0.587  ENFP   \n",
       "7          g24JGYuT74A.004.mp4  0.590  0.399  0.410  0.532  0.507  ESFP   \n",
       "8          JZNMxa3OKHY.000.mp4  0.606  0.524  0.531  0.594  0.580  ENFP   \n",
       "9          nvlqJbHk_Lc.003.mp4  0.511  0.465  0.391  0.444  0.439  INFP   \n",
       "10         _plk5k7PBEg.003.mp4  0.648  0.610  0.525  0.614  0.606  ENFP   \n",
       "\n",
       "                     Disorder 1            Disorder 2            Disorder 3  \n",
       "Person ID                                                                    \n",
       "1          Narcissistic (0.034)      Paranoid (0.033)     Dependent (0.028)  \n",
       "2              Schizoid (0.039)      Avoidant (0.022)     Dependent (0.012)  \n",
       "3             Dependent (0.089)  Narcissistic (0.087)      Paranoid (0.082)  \n",
       "4           Narcissistic (0.03)      Paranoid (0.029)     Dependent (0.025)  \n",
       "5              Paranoid (0.067)  Narcissistic (0.064)    Histrionic (0.062)  \n",
       "6              Paranoid (0.063)     Histrionic (0.06)   Narcissistic (0.06)  \n",
       "7           Narcissistic (0.09)      Paranoid (0.085)     Dependent (0.074)  \n",
       "8              Paranoid (0.061)  Narcissistic (0.058)    Histrionic (0.057)  \n",
       "9              Avoidant (0.036)      Schizoid (0.035)  Narcissistic (0.031)  \n",
       "10              Paranoid (0.06)    Histrionic (0.057)  Narcissistic (0.056)  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "_b5._colleague_personality_desorders(\n",
    "    correlation_coefficients_mbti = df_correlation_coefficients_mbti,\n",
    "    correlation_coefficients_disorders = df_correlation_coefficients_disorders,\n",
    "    personality_desorder_number = 3,\n",
    "    threshold = 0.5,\n",
    "    out = True\n",
    ")\n",
    "\n",
    "_b5._save_logs(df = _b5.df_files_MBTI_disorders_, name = 'MBTI_colleague_personality_type_match_fi_en', out = True)\n",
    "\n",
    "# Optional\n",
    "df = _b5.df_files_MBTI_disorders_.rename(columns = {'Openness':'OPE', 'Conscientiousness':'CON', 'Extraversion': 'EXT', 'Agreeableness': 'AGR', 'Non-Neuroticism': 'NNEU'})\n",
    "columns_to_round = df.columns[1:6]\n",
    "df[columns_to_round] = df[columns_to_round].apply(lambda x: [round(i, 3) for i in x])\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "180a973a-c4ff-43d0-89ef-a809cd6ac00b",
   "metadata": {},
   "source": [
    "### `MuPTA` (ru)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "69c7845d-be20-4632-bd83-bbbf9d47f0f0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/markdown": [
       "<span style=\"color:#666\">**[</span><span style=\"color:#1776D2\">2024-10-10 21:54:06</span><span style=\"color:#666\">]</span> <span style=\"color:#666\">Извлечение признаков (экспертных и нейросетевых) из текста ...</span>** "
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "<span style=\"color:#666\">**[</span><span style=\"color:#1776D2\">2024-10-10 21:54:07</span><span style=\"color:#666\">]</span> <span style=\"color:#666\">Получение прогнозов и вычисление точности (мультимодальное объединение) ...</span>**<p><span style=\"color:#666\">&nbsp;&nbsp;&nbsp;&nbsp;10 из 10 (100.0%) ... GitHub\\OCEANAI\\docs\\source\\user_guide\\notebooks\\video_MuPTA\\test\\speaker_27_center_83.mov ...</span></p>"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
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       "\n",
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       "        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>Path</th>\n",
       "      <th>Openness</th>\n",
       "      <th>Conscientiousness</th>\n",
       "      <th>Extraversion</th>\n",
       "      <th>Agreeableness</th>\n",
       "      <th>Non-Neuroticism</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Person ID</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>speaker_01_center_83.mov</td>\n",
       "      <td>0.765745</td>\n",
       "      <td>0.696637</td>\n",
       "      <td>0.656309</td>\n",
       "      <td>0.75986</td>\n",
       "      <td>0.494141</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>speaker_06_center_83.mov</td>\n",
       "      <td>0.686514</td>\n",
       "      <td>0.659488</td>\n",
       "      <td>0.611838</td>\n",
       "      <td>0.749739</td>\n",
       "      <td>0.420672</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>speaker_07_center_83.mov</td>\n",
       "      <td>0.671993</td>\n",
       "      <td>0.661216</td>\n",
       "      <td>0.571759</td>\n",
       "      <td>0.704542</td>\n",
       "      <td>0.381026</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>speaker_10_center_83.mov</td>\n",
       "      <td>0.69828</td>\n",
       "      <td>0.59893</td>\n",
       "      <td>0.571893</td>\n",
       "      <td>0.674907</td>\n",
       "      <td>0.35082</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>speaker_11_center_83.mov</td>\n",
       "      <td>0.718329</td>\n",
       "      <td>0.598986</td>\n",
       "      <td>0.573518</td>\n",
       "      <td>0.73201</td>\n",
       "      <td>0.379845</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>speaker_15_center_83.mov</td>\n",
       "      <td>0.670932</td>\n",
       "      <td>0.671055</td>\n",
       "      <td>0.602337</td>\n",
       "      <td>0.708656</td>\n",
       "      <td>0.399527</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>speaker_19_center_83.mov</td>\n",
       "      <td>0.767261</td>\n",
       "      <td>0.658167</td>\n",
       "      <td>0.653367</td>\n",
       "      <td>0.801366</td>\n",
       "      <td>0.463443</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>speaker_23_center_83.mov</td>\n",
       "      <td>0.699837</td>\n",
       "      <td>0.684907</td>\n",
       "      <td>0.616671</td>\n",
       "      <td>0.806437</td>\n",
       "      <td>0.447853</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>speaker_24_center_83.mov</td>\n",
       "      <td>0.710566</td>\n",
       "      <td>0.66299</td>\n",
       "      <td>0.610562</td>\n",
       "      <td>0.711242</td>\n",
       "      <td>0.413696</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>speaker_27_center_83.mov</td>\n",
       "      <td>0.759404</td>\n",
       "      <td>0.712562</td>\n",
       "      <td>0.658357</td>\n",
       "      <td>0.830507</td>\n",
       "      <td>0.507612</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                               Path  Openness Conscientiousness Extraversion  \\\n",
       "Person ID                                                                      \n",
       "1          speaker_01_center_83.mov  0.765745          0.696637     0.656309   \n",
       "2          speaker_06_center_83.mov  0.686514          0.659488     0.611838   \n",
       "3          speaker_07_center_83.mov  0.671993          0.661216     0.571759   \n",
       "4          speaker_10_center_83.mov   0.69828           0.59893     0.571893   \n",
       "5          speaker_11_center_83.mov  0.718329          0.598986     0.573518   \n",
       "6          speaker_15_center_83.mov  0.670932          0.671055     0.602337   \n",
       "7          speaker_19_center_83.mov  0.767261          0.658167     0.653367   \n",
       "8          speaker_23_center_83.mov  0.699837          0.684907     0.616671   \n",
       "9          speaker_24_center_83.mov  0.710566           0.66299     0.610562   \n",
       "10         speaker_27_center_83.mov  0.759404          0.712562     0.658357   \n",
       "\n",
       "          Agreeableness Non-Neuroticism  \n",
       "Person ID                                \n",
       "1               0.75986        0.494141  \n",
       "2              0.749739        0.420672  \n",
       "3              0.704542        0.381026  \n",
       "4              0.674907         0.35082  \n",
       "5               0.73201        0.379845  \n",
       "6              0.708656        0.399527  \n",
       "7              0.801366        0.463443  \n",
       "8              0.806437        0.447853  \n",
       "9              0.711242        0.413696  \n",
       "10             0.830507        0.507612  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "<span style=\"color:#666\">**[</span><span style=\"color:#1776D2\">2024-10-10 21:54:07</span><span style=\"color:#666\">]</span> <span style=\"color:#666\">Точность по отдельным персональным качествам личности человека ...</span>** "
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Openness</th>\n",
       "      <th>Conscientiousness</th>\n",
       "      <th>Extraversion</th>\n",
       "      <th>Agreeableness</th>\n",
       "      <th>Non-Neuroticism</th>\n",
       "      <th>Mean</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Metrics</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>MAE</th>\n",
       "      <td>0.0706</td>\n",
       "      <td>0.0788</td>\n",
       "      <td>0.1328</td>\n",
       "      <td>0.1071</td>\n",
       "      <td>0.1002</td>\n",
       "      <td>0.0979</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Accuracy</th>\n",
       "      <td>0.9294</td>\n",
       "      <td>0.9212</td>\n",
       "      <td>0.8672</td>\n",
       "      <td>0.8929</td>\n",
       "      <td>0.8998</td>\n",
       "      <td>0.9021</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          Openness  Conscientiousness  Extraversion  Agreeableness  \\\n",
       "Metrics                                                              \n",
       "MAE         0.0706             0.0788        0.1328         0.1071   \n",
       "Accuracy    0.9294             0.9212        0.8672         0.8929   \n",
       "\n",
       "          Non-Neuroticism    Mean  \n",
       "Metrics                            \n",
       "MAE                0.1002  0.0979  \n",
       "Accuracy           0.8998  0.9021  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "<span style=\"color:#666\">**[</span><span style=\"color:#1776D2\">2024-10-10 21:54:07</span><span style=\"color:#666\">]</span> <span style=\"color:#666\">Средняя средних абсолютных ошибок: <span style=\"color:#1776D2\">0.0979</span>, средняя точность: <span style=\"color:#1776D2\">0.9021</span> ...</span>** "
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "<span style=\"color:#008001\">**Лог файлы успешно сохранены ...**</span>"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "<span style=\"color:#666\">**--- Время выполнения: 311.791 сек. ---**</span>"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import os\n",
    "import pandas as pd\n",
    "\n",
    "# Импорт модуля\n",
    "from oceanai.modules.lab.build import Run\n",
    "\n",
    "# Создание экземпляра класса\n",
    "_b5 = Run()\n",
    "\n",
    "corpus = 'mupta'\n",
    "lang = 'ru'\n",
    "\n",
    "# Настройка ядра\n",
    "_b5.path_to_save_ = './models' # Директория для сохранения файла\n",
    "_b5.chunk_size_ = 2000000      # Размер загрузки файла из сети за 1 шаг\n",
    "\n",
    "# Формирование аудиомоделей\n",
    "res_load_model_hc = _b5.load_audio_model_hc()\n",
    "res_load_model_nn = _b5.load_audio_model_nn()\n",
    "\n",
    "# Загрузка весов аудиомоделей\n",
    "url = _b5.weights_for_big5_['audio'][corpus]['hc']['googledisk']\n",
    "res_load_model_weights_hc = _b5.load_audio_model_weights_hc(url = url, force_reload = False)\n",
    "\n",
    "url = _b5.weights_for_big5_['audio'][corpus]['nn']['googledisk']\n",
    "res_load_model_weights_nn = _b5.load_audio_model_weights_nn(url = url, force_reload = False)\n",
    "\n",
    "# Формирование видеомоделей\n",
    "res_load_model_hc = _b5.load_video_model_hc(lang=lang)\n",
    "res_load_model_deep_fe = _b5.load_video_model_deep_fe()\n",
    "res_load_model_nn = _b5.load_video_model_nn()\n",
    "\n",
    "# Загрузка весов видеомоделей\n",
    "url = _b5.weights_for_big5_['video'][corpus]['hc']['googledisk']\n",
    "res_load_model_weights_hc = _b5.load_video_model_weights_hc(url = url, force_reload = False)\n",
    "\n",
    "url = _b5.weights_for_big5_['video'][corpus]['fe']['googledisk']\n",
    "res_load_model_weights_deep_fe = _b5.load_video_model_weights_deep_fe(url = url, force_reload = False)\n",
    "\n",
    "url = _b5.weights_for_big5_['video'][corpus]['nn']['googledisk']\n",
    "res_load_model_weights_nn = _b5.load_video_model_weights_nn(url = url, force_reload = False)\n",
    "\n",
    "# Загрузка словаря с экспертными признаками (текстовая модальность)\n",
    "res_load_text_features = _b5.load_text_features()\n",
    "\n",
    "# Формирование текстовых моделей \n",
    "res_setup_translation_model = _b5.setup_translation_model() # только для русского языка\n",
    "res_setup_translation_model = _b5.setup_bert_encoder(force_reload = False)\n",
    "res_load_text_model_hc_fi = _b5.load_text_model_hc(corpus=corpus)\n",
    "res_load_text_model_nn_fi = _b5.load_text_model_nn(corpus=corpus)\n",
    "\n",
    "# Загрузка весов текстовых моделей\n",
    "url = _b5.weights_for_big5_['text'][corpus]['hc']['googledisk']\n",
    "res_load_text_model_weights_hc_fi = _b5.load_text_model_weights_hc(url = url, force_reload = False)\n",
    "\n",
    "url = _b5.weights_for_big5_['text'][corpus]['nn']['googledisk']\n",
    "res_load_text_model_weights_nn_fi = _b5.load_text_model_weights_nn(url = url, force_reload = False)\n",
    "\n",
    "# Формирование модели для мультимодального объединения информации\n",
    "res_load_avt_model_b5 = _b5.load_avt_model_b5()\n",
    "\n",
    "# Загрузка весов модели для мультимодального объединения информации\n",
    "url = _b5.weights_for_big5_['avt'][corpus]['b5']['googledisk']\n",
    "res_load_avt_model_weights_b5 = _b5.load_avt_model_weights_b5(url = url, force_reload = False)\n",
    "\n",
    "PATH_TO_DIR = './video_MuPTA/'\n",
    "PATH_SAVE_VIDEO = './video_MuPTA/test/'\n",
    "\n",
    "_b5.path_to_save_ = PATH_SAVE_VIDEO\n",
    "\n",
    "# Загрузка 10 тестовых аудиовидеозаписей из корпуса MuPTA\n",
    "# URL: https://hci.nw.ru/en/pages/mupta-corpus\n",
    "domain = 'https://download.sberdisk.ru/download/file/'\n",
    "tets_name_files = [\n",
    "    '477995979?token=2cvyk7CS0mHx2MJ&filename=speaker_06_center_83.mov',\n",
    "    '477995980?token=jGPtBPS69uzFU6Y&filename=speaker_01_center_83.mov',\n",
    "    '477995967?token=zCaRbNB6ht5wMPq&filename=speaker_11_center_83.mov',\n",
    "    '477995966?token=B1rbinDYRQKrI3T&filename=speaker_15_center_83.mov',\n",
    "    '477995978?token=dEpVDtZg1EQiEQ9&filename=speaker_07_center_83.mov',\n",
    "    '477995961?token=o1hVjw8G45q9L9Z&filename=speaker_19_center_83.mov',\n",
    "    '477995964?token=5K220Aqf673VHPq&filename=speaker_23_center_83.mov',\n",
    "    '477995965?token=v1LVD2KT1cU7Lpb&filename=speaker_24_center_83.mov',\n",
    "    '477995962?token=tmaSGyyWLA6XCy9&filename=speaker_27_center_83.mov',\n",
    "    '477995963?token=bTpo96qNDPcwGqb&filename=speaker_10_center_83.mov',\n",
    "]\n",
    "\n",
    "for curr_files in tets_name_files:\n",
    "    _b5.download_file_from_url(url = domain + curr_files, out = True)\n",
    "\n",
    "# Получение прогнозов\n",
    "_b5.path_to_dataset_ = PATH_TO_DIR # Директория набора данных\n",
    "_b5.ext_ = ['.mov'] # Расширения искомых файлов\n",
    "\n",
    "# Полный путь к файлу с верными предсказаниями для подсчета точности\n",
    "url_accuracy = _b5.true_traits_['mupta']['googledisk']\n",
    "\n",
    "_b5.get_avt_predictions(url_accuracy = url_accuracy, lang = lang)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "47f3fd32-1f6a-4d75-b3fb-f4deae84d996",
   "metadata": {},
   "source": [
    "<hr>\n",
    "\n",
    "Для поиска подходящего коллеги по работе необходимо знать по два коэффициента корреляции для каждого персонального качества личности человека. Эти коэффициенты должны показывать, как изменится оценка качества одного человека, если она будет больше или меньше оценки качества другого человека.\n",
    "\n",
    "В качестве примера предлагается использование коэффициентов корреляции между двумя людьми в контексте отношений \"начальник-подчиненный\", представленных в статье:\n",
    "\n",
    "1) Kuroda S., Yamamoto I. Good boss, bad boss, workers’ mental health and productivity: Evidence from Japan // Japan & The World Economy. – 2018. – vol. 48. – pp. 106-118.\n",
    "\n",
    "Пользователь может установить свои коэффициенты корреляции"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "92f637ee-2575-49bd-ac17-fc651c906c84",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Score_comparison</th>\n",
       "      <th>Openness</th>\n",
       "      <th>Conscientiousness</th>\n",
       "      <th>Extraversion</th>\n",
       "      <th>Agreeableness</th>\n",
       "      <th>Non-Neuroticism</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ID</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>higher</td>\n",
       "      <td>-0.0602</td>\n",
       "      <td>0.0471</td>\n",
       "      <td>-0.1070</td>\n",
       "      <td>-0.0832</td>\n",
       "      <td>0.190</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>lower</td>\n",
       "      <td>-0.1720</td>\n",
       "      <td>-0.1050</td>\n",
       "      <td>0.0772</td>\n",
       "      <td>0.0703</td>\n",
       "      <td>-0.229</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Score_comparison  Openness  Conscientiousness  Extraversion  Agreeableness  \\\n",
       "ID                                                                              \n",
       "1            higher   -0.0602             0.0471       -0.1070        -0.0832   \n",
       "2             lower   -0.1720            -0.1050        0.0772         0.0703   \n",
       "\n",
       "    Non-Neuroticism  \n",
       "ID                   \n",
       "1             0.190  \n",
       "2            -0.229  "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Загрузка датафрейма с коэффициентами корреляции\n",
    "url = 'https://download.sberdisk.ru/download/file/478675819?token=LuB7L1QsEY0UuSs&filename=colleague_ranking.csv'\n",
    "df_correlation_coefficients = pd.read_csv(url)\n",
    "df_correlation_coefficients = pd.DataFrame(\n",
    "    df_correlation_coefficients.drop(['ID'], axis = 1)\n",
    ")\n",
    "\n",
    "df_correlation_coefficients.index.name = 'ID'\n",
    "df_correlation_coefficients.index += 1\n",
    "df_correlation_coefficients.index = df_correlation_coefficients.index.map(str)\n",
    "\n",
    "df_correlation_coefficients"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7830c543-c445-438b-ad95-e27077ed52ca",
   "metadata": {},
   "source": [
    "#### Поиск старшего коллеги"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "8d39a185-9c2a-4621-8107-a6550ea8de9a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Path</th>\n",
       "      <th>OPE</th>\n",
       "      <th>CON</th>\n",
       "      <th>EXT</th>\n",
       "      <th>AGR</th>\n",
       "      <th>NNEU</th>\n",
       "      <th>Match</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Person ID</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>speaker_01_center_83.mov</td>\n",
       "      <td>0.766</td>\n",
       "      <td>0.697</td>\n",
       "      <td>0.656</td>\n",
       "      <td>0.760</td>\n",
       "      <td>0.494</td>\n",
       "      <td>-0.053</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>speaker_27_center_83.mov</td>\n",
       "      <td>0.759</td>\n",
       "      <td>0.713</td>\n",
       "      <td>0.658</td>\n",
       "      <td>0.831</td>\n",
       "      <td>0.508</td>\n",
       "      <td>-0.055</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>speaker_23_center_83.mov</td>\n",
       "      <td>0.700</td>\n",
       "      <td>0.685</td>\n",
       "      <td>0.617</td>\n",
       "      <td>0.806</td>\n",
       "      <td>0.448</td>\n",
       "      <td>-0.058</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>speaker_19_center_83.mov</td>\n",
       "      <td>0.767</td>\n",
       "      <td>0.658</td>\n",
       "      <td>0.653</td>\n",
       "      <td>0.801</td>\n",
       "      <td>0.463</td>\n",
       "      <td>-0.064</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>speaker_10_center_83.mov</td>\n",
       "      <td>0.698</td>\n",
       "      <td>0.599</td>\n",
       "      <td>0.572</td>\n",
       "      <td>0.675</td>\n",
       "      <td>0.351</td>\n",
       "      <td>-0.211</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>speaker_07_center_83.mov</td>\n",
       "      <td>0.672</td>\n",
       "      <td>0.661</td>\n",
       "      <td>0.572</td>\n",
       "      <td>0.705</td>\n",
       "      <td>0.381</td>\n",
       "      <td>-0.216</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>speaker_15_center_83.mov</td>\n",
       "      <td>0.671</td>\n",
       "      <td>0.671</td>\n",
       "      <td>0.602</td>\n",
       "      <td>0.709</td>\n",
       "      <td>0.400</td>\n",
       "      <td>-0.224</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>speaker_11_center_83.mov</td>\n",
       "      <td>0.718</td>\n",
       "      <td>0.599</td>\n",
       "      <td>0.574</td>\n",
       "      <td>0.732</td>\n",
       "      <td>0.380</td>\n",
       "      <td>-0.224</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>speaker_24_center_83.mov</td>\n",
       "      <td>0.711</td>\n",
       "      <td>0.663</td>\n",
       "      <td>0.611</td>\n",
       "      <td>0.711</td>\n",
       "      <td>0.414</td>\n",
       "      <td>-0.231</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>speaker_06_center_83.mov</td>\n",
       "      <td>0.687</td>\n",
       "      <td>0.659</td>\n",
       "      <td>0.612</td>\n",
       "      <td>0.750</td>\n",
       "      <td>0.421</td>\n",
       "      <td>-0.234</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                               Path    OPE    CON    EXT    AGR   NNEU  Match\n",
       "Person ID                                                                    \n",
       "1          speaker_01_center_83.mov  0.766  0.697  0.656  0.760  0.494 -0.053\n",
       "10         speaker_27_center_83.mov  0.759  0.713  0.658  0.831  0.508 -0.055\n",
       "8          speaker_23_center_83.mov  0.700  0.685  0.617  0.806  0.448 -0.058\n",
       "7          speaker_19_center_83.mov  0.767  0.658  0.653  0.801  0.463 -0.064\n",
       "4          speaker_10_center_83.mov  0.698  0.599  0.572  0.675  0.351 -0.211\n",
       "3          speaker_07_center_83.mov  0.672  0.661  0.572  0.705  0.381 -0.216\n",
       "6          speaker_15_center_83.mov  0.671  0.671  0.602  0.709  0.400 -0.224\n",
       "5          speaker_11_center_83.mov  0.718  0.599  0.574  0.732  0.380 -0.224\n",
       "9          speaker_24_center_83.mov  0.711  0.663  0.611  0.711  0.414 -0.231\n",
       "2          speaker_06_center_83.mov  0.687  0.659  0.612  0.750  0.421 -0.234"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Список оценок персональных качеств личности целевого человека\n",
    "target_scores = [0.527886, 0.522337, 0.458468, 0.51761, 0.444649]\n",
    "\n",
    "_b5._colleague_ranking(\n",
    "    correlation_coefficients = df_correlation_coefficients,\n",
    "    target_scores = target_scores,\n",
    "    colleague = 'major',\n",
    "    equal_coefficients = 0.5,\n",
    "    out = False\n",
    ")\n",
    "\n",
    "_b5._save_logs(df = _b5.df_files_colleague_, name = 'major_colleague_ranking_mupta_ru', out = True)\n",
    "\n",
    "# Опционно\n",
    "df = _b5.df_files_colleague_.rename(columns = {'Openness':'OPE', 'Conscientiousness':'CON', 'Extraversion': 'EXT', 'Agreeableness': 'AGR', 'Non-Neuroticism': 'NNEU'})\n",
    "columns_to_round = df.columns[1:]\n",
    "df[columns_to_round] = df[columns_to_round].apply(lambda x: [round(i, 3) for i in x])\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7dc09967-1654-417b-bef3-2d7aba261fe5",
   "metadata": {},
   "source": [
    "#### Поиск младшего коллеги"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "a43376a5-c738-4a8a-85f6-96efe6a02ad5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\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>Path</th>\n",
       "      <th>OPE</th>\n",
       "      <th>CON</th>\n",
       "      <th>EXT</th>\n",
       "      <th>AGR</th>\n",
       "      <th>NNEU</th>\n",
       "      <th>Match</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Person ID</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>speaker_06_center_83.mov</td>\n",
       "      <td>0.687</td>\n",
       "      <td>0.659</td>\n",
       "      <td>0.612</td>\n",
       "      <td>0.750</td>\n",
       "      <td>0.421</td>\n",
       "      <td>-0.007</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>speaker_15_center_83.mov</td>\n",
       "      <td>0.671</td>\n",
       "      <td>0.671</td>\n",
       "      <td>0.602</td>\n",
       "      <td>0.709</td>\n",
       "      <td>0.400</td>\n",
       "      <td>-0.014</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>speaker_24_center_83.mov</td>\n",
       "      <td>0.711</td>\n",
       "      <td>0.663</td>\n",
       "      <td>0.611</td>\n",
       "      <td>0.711</td>\n",
       "      <td>0.414</td>\n",
       "      <td>-0.016</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>speaker_11_center_83.mov</td>\n",
       "      <td>0.718</td>\n",
       "      <td>0.599</td>\n",
       "      <td>0.574</td>\n",
       "      <td>0.732</td>\n",
       "      <td>0.380</td>\n",
       "      <td>-0.019</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>speaker_07_center_83.mov</td>\n",
       "      <td>0.672</td>\n",
       "      <td>0.661</td>\n",
       "      <td>0.572</td>\n",
       "      <td>0.705</td>\n",
       "      <td>0.381</td>\n",
       "      <td>-0.019</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>speaker_10_center_83.mov</td>\n",
       "      <td>0.698</td>\n",
       "      <td>0.599</td>\n",
       "      <td>0.572</td>\n",
       "      <td>0.675</td>\n",
       "      <td>0.351</td>\n",
       "      <td>-0.025</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>speaker_23_center_83.mov</td>\n",
       "      <td>0.700</td>\n",
       "      <td>0.685</td>\n",
       "      <td>0.617</td>\n",
       "      <td>0.806</td>\n",
       "      <td>0.448</td>\n",
       "      <td>-0.191</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>speaker_19_center_83.mov</td>\n",
       "      <td>0.767</td>\n",
       "      <td>0.658</td>\n",
       "      <td>0.653</td>\n",
       "      <td>0.801</td>\n",
       "      <td>0.463</td>\n",
       "      <td>-0.200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>speaker_27_center_83.mov</td>\n",
       "      <td>0.759</td>\n",
       "      <td>0.713</td>\n",
       "      <td>0.658</td>\n",
       "      <td>0.831</td>\n",
       "      <td>0.508</td>\n",
       "      <td>-0.212</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>speaker_01_center_83.mov</td>\n",
       "      <td>0.766</td>\n",
       "      <td>0.697</td>\n",
       "      <td>0.656</td>\n",
       "      <td>0.760</td>\n",
       "      <td>0.494</td>\n",
       "      <td>-0.214</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                               Path    OPE    CON    EXT    AGR   NNEU  Match\n",
       "Person ID                                                                    \n",
       "2          speaker_06_center_83.mov  0.687  0.659  0.612  0.750  0.421 -0.007\n",
       "6          speaker_15_center_83.mov  0.671  0.671  0.602  0.709  0.400 -0.014\n",
       "9          speaker_24_center_83.mov  0.711  0.663  0.611  0.711  0.414 -0.016\n",
       "5          speaker_11_center_83.mov  0.718  0.599  0.574  0.732  0.380 -0.019\n",
       "3          speaker_07_center_83.mov  0.672  0.661  0.572  0.705  0.381 -0.019\n",
       "4          speaker_10_center_83.mov  0.698  0.599  0.572  0.675  0.351 -0.025\n",
       "8          speaker_23_center_83.mov  0.700  0.685  0.617  0.806  0.448 -0.191\n",
       "7          speaker_19_center_83.mov  0.767  0.658  0.653  0.801  0.463 -0.200\n",
       "10         speaker_27_center_83.mov  0.759  0.713  0.658  0.831  0.508 -0.212\n",
       "1          speaker_01_center_83.mov  0.766  0.697  0.656  0.760  0.494 -0.214"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Список оценок персональных качеств личности целевого человека\n",
    "target_scores = [0.527886, 0.522337, 0.458468, 0.51761, 0.444649]\n",
    "\n",
    "_b5._colleague_ranking(\n",
    "    correlation_coefficients = df_correlation_coefficients,\n",
    "    target_scores = target_scores,\n",
    "    colleague = 'minor',\n",
    "    equal_coefficients = 0.5,\n",
    "    out = False\n",
    ")\n",
    "\n",
    "_b5._save_logs(df = _b5.df_files_colleague_, name = 'minor_colleague_ranking_mupta_ru', out = True)\n",
    "\n",
    "# Опционно\n",
    "df = _b5.df_files_colleague_.rename(columns = {'Openness':'OPE', 'Conscientiousness':'CON', 'Extraversion': 'EXT', 'Agreeableness': 'AGR', 'Non-Neuroticism': 'NNEU'})\n",
    "columns_to_round = df.columns[1:]\n",
    "df[columns_to_round] = df[columns_to_round].apply(lambda x: [round(i, 3) for i in x])\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a032ddef",
   "metadata": {},
   "source": [
    "<hr>\n",
    "\n",
    "Для поиска подходящего коллеги по типу личности MBTI необходимо знать коэффициенты корреляции между личностными качествами человека и типами MBTI, а также оценки этих качеств для целевого человека.\n",
    "\n",
    "В качестве примера предлагается использование коэффициентов корреляции, представленных в статье:\n",
    "\n",
    "1) Furnham A. The big five facets and the MBTI: The relationship between the 30 NEO-PI (R) Facets and the four Myers-Briggs Type Indicator (MBTI) scores // Psychology. - 2022. vol. 13(10). - pp. 1504-1516.\n",
    "\n",
    "Пользователь может установить свои коэффициенты корреляции"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "c47ab83d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Trait</th>\n",
       "      <th>EI</th>\n",
       "      <th>SN</th>\n",
       "      <th>TF</th>\n",
       "      <th>JP</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ID</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Openness</td>\n",
       "      <td>0.09</td>\n",
       "      <td>-0.03</td>\n",
       "      <td>-0.14</td>\n",
       "      <td>-0.16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Conscientiousness</td>\n",
       "      <td>0.04</td>\n",
       "      <td>-0.04</td>\n",
       "      <td>0.20</td>\n",
       "      <td>0.14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Extraversion</td>\n",
       "      <td>0.20</td>\n",
       "      <td>-0.03</td>\n",
       "      <td>0.01</td>\n",
       "      <td>-0.07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Agreeableness</td>\n",
       "      <td>0.02</td>\n",
       "      <td>0.05</td>\n",
       "      <td>-0.35</td>\n",
       "      <td>0.03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Non-Neuroticism</td>\n",
       "      <td>0.08</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.16</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                Trait    EI    SN    TF    JP\n",
       "ID                                           \n",
       "1            Openness  0.09 -0.03 -0.14 -0.16\n",
       "2   Conscientiousness  0.04 -0.04  0.20  0.14\n",
       "3        Extraversion  0.20 -0.03  0.01 -0.07\n",
       "4       Agreeableness  0.02  0.05 -0.35  0.03\n",
       "5     Non-Neuroticism  0.08  0.00  0.16  0.00"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Загрузка датафрейма с коэффициентами корреляции\n",
    "url = 'https://download.sberdisk.ru/download/file/493644095?token=EX7hFxNJhMoLumI&filename=df_mbti_correlation.csv'\n",
    "df_correlation_coefficients = pd.read_csv(url)\n",
    "\n",
    "df_correlation_coefficients.index.name = 'ID'\n",
    "df_correlation_coefficients.index += 1\n",
    "df_correlation_coefficients.index = df_correlation_coefficients.index.map(str)\n",
    "\n",
    "df_correlation_coefficients"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "31444218",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\n",
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       "        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>Path</th>\n",
       "      <th>OPE</th>\n",
       "      <th>CON</th>\n",
       "      <th>EXT</th>\n",
       "      <th>AGR</th>\n",
       "      <th>NNEU</th>\n",
       "      <th>EI</th>\n",
       "      <th>SN</th>\n",
       "      <th>TF</th>\n",
       "      <th>JP</th>\n",
       "      <th>MBTI</th>\n",
       "      <th>Match</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Person ID</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>speaker_01_center_83.mov</td>\n",
       "      <td>0.766</td>\n",
       "      <td>0.697</td>\n",
       "      <td>0.656</td>\n",
       "      <td>0.760</td>\n",
       "      <td>0.494</td>\n",
       "      <td>0.203710</td>\n",
       "      <td>-0.032534</td>\n",
       "      <td>-0.306328</td>\n",
       "      <td>-0.048136</td>\n",
       "      <td>ENFP</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>speaker_06_center_83.mov</td>\n",
       "      <td>0.687</td>\n",
       "      <td>0.659</td>\n",
       "      <td>0.612</td>\n",
       "      <td>0.750</td>\n",
       "      <td>0.421</td>\n",
       "      <td>0.191874</td>\n",
       "      <td>-0.027843</td>\n",
       "      <td>-0.287812</td>\n",
       "      <td>-0.037850</td>\n",
       "      <td>ENFP</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>speaker_07_center_83.mov</td>\n",
       "      <td>0.672</td>\n",
       "      <td>0.661</td>\n",
       "      <td>0.572</td>\n",
       "      <td>0.705</td>\n",
       "      <td>0.381</td>\n",
       "      <td>0.184889</td>\n",
       "      <td>-0.028534</td>\n",
       "      <td>-0.263672</td>\n",
       "      <td>-0.033835</td>\n",
       "      <td>ENFP</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>speaker_10_center_83.mov</td>\n",
       "      <td>0.698</td>\n",
       "      <td>0.599</td>\n",
       "      <td>0.572</td>\n",
       "      <td>0.675</td>\n",
       "      <td>0.351</td>\n",
       "      <td>0.186613</td>\n",
       "      <td>-0.028317</td>\n",
       "      <td>-0.264603</td>\n",
       "      <td>-0.047660</td>\n",
       "      <td>ENFP</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>speaker_11_center_83.mov</td>\n",
       "      <td>0.718</td>\n",
       "      <td>0.599</td>\n",
       "      <td>0.574</td>\n",
       "      <td>0.732</td>\n",
       "      <td>0.380</td>\n",
       "      <td>0.187565</td>\n",
       "      <td>-0.026114</td>\n",
       "      <td>-0.292012</td>\n",
       "      <td>-0.049261</td>\n",
       "      <td>ENFP</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>speaker_15_center_83.mov</td>\n",
       "      <td>0.671</td>\n",
       "      <td>0.671</td>\n",
       "      <td>0.602</td>\n",
       "      <td>0.709</td>\n",
       "      <td>0.400</td>\n",
       "      <td>0.189904</td>\n",
       "      <td>-0.029607</td>\n",
       "      <td>-0.265650</td>\n",
       "      <td>-0.034305</td>\n",
       "      <td>ENFP</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>speaker_19_center_83.mov</td>\n",
       "      <td>0.767</td>\n",
       "      <td>0.658</td>\n",
       "      <td>0.653</td>\n",
       "      <td>0.801</td>\n",
       "      <td>0.463</td>\n",
       "      <td>0.205006</td>\n",
       "      <td>-0.028877</td>\n",
       "      <td>-0.323879</td>\n",
       "      <td>-0.052313</td>\n",
       "      <td>ENFP</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>speaker_23_center_83.mov</td>\n",
       "      <td>0.700</td>\n",
       "      <td>0.685</td>\n",
       "      <td>0.617</td>\n",
       "      <td>0.806</td>\n",
       "      <td>0.448</td>\n",
       "      <td>0.194016</td>\n",
       "      <td>-0.026570</td>\n",
       "      <td>-0.308738</td>\n",
       "      <td>-0.035061</td>\n",
       "      <td>ENFP</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>speaker_24_center_83.mov</td>\n",
       "      <td>0.711</td>\n",
       "      <td>0.663</td>\n",
       "      <td>0.611</td>\n",
       "      <td>0.711</td>\n",
       "      <td>0.414</td>\n",
       "      <td>0.193712</td>\n",
       "      <td>-0.030591</td>\n",
       "      <td>-0.275902</td>\n",
       "      <td>-0.042274</td>\n",
       "      <td>ENFP</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>speaker_27_center_83.mov</td>\n",
       "      <td>0.759</td>\n",
       "      <td>0.713</td>\n",
       "      <td>0.658</td>\n",
       "      <td>0.831</td>\n",
       "      <td>0.508</td>\n",
       "      <td>0.285739</td>\n",
       "      <td>-0.029510</td>\n",
       "      <td>-0.166680</td>\n",
       "      <td>-0.042916</td>\n",
       "      <td>ENFP</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                               Path    OPE    CON    EXT    AGR   NNEU  \\\n",
       "Person ID                                                                \n",
       "1          speaker_01_center_83.mov  0.766  0.697  0.656  0.760  0.494   \n",
       "2          speaker_06_center_83.mov  0.687  0.659  0.612  0.750  0.421   \n",
       "3          speaker_07_center_83.mov  0.672  0.661  0.572  0.705  0.381   \n",
       "4          speaker_10_center_83.mov  0.698  0.599  0.572  0.675  0.351   \n",
       "5          speaker_11_center_83.mov  0.718  0.599  0.574  0.732  0.380   \n",
       "6          speaker_15_center_83.mov  0.671  0.671  0.602  0.709  0.400   \n",
       "7          speaker_19_center_83.mov  0.767  0.658  0.653  0.801  0.463   \n",
       "8          speaker_23_center_83.mov  0.700  0.685  0.617  0.806  0.448   \n",
       "9          speaker_24_center_83.mov  0.711  0.663  0.611  0.711  0.414   \n",
       "10         speaker_27_center_83.mov  0.759  0.713  0.658  0.831  0.508   \n",
       "\n",
       "                 EI        SN        TF        JP  MBTI  Match  \n",
       "Person ID                                                       \n",
       "1          0.203710 -0.032534 -0.306328 -0.048136  ENFP    0.0  \n",
       "2          0.191874 -0.027843 -0.287812 -0.037850  ENFP    0.0  \n",
       "3          0.184889 -0.028534 -0.263672 -0.033835  ENFP    0.0  \n",
       "4          0.186613 -0.028317 -0.264603 -0.047660  ENFP    0.0  \n",
       "5          0.187565 -0.026114 -0.292012 -0.049261  ENFP    0.0  \n",
       "6          0.189904 -0.029607 -0.265650 -0.034305  ENFP    0.0  \n",
       "7          0.205006 -0.028877 -0.323879 -0.052313  ENFP    0.0  \n",
       "8          0.194016 -0.026570 -0.308738 -0.035061  ENFP    0.0  \n",
       "9          0.193712 -0.030591 -0.275902 -0.042274  ENFP    0.0  \n",
       "10         0.285739 -0.029510 -0.166680 -0.042916  ENFP    0.0  "
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "_b5._colleague_personality_type_match(\n",
    "    correlation_coefficients = df_correlation_coefficients,\n",
    "    target_scores = [0.34, 0.56, 0.42, 0.57, 0.56],\n",
    "    threshold = 0.5,\n",
    "    out = False\n",
    ")\n",
    "\n",
    "_b5._save_logs(df = _b5.df_files_MBTI_colleague_match_, name = 'MBTI_colleague_personality_type_match_ru', out = True)\n",
    "\n",
    "# Optional\n",
    "df = _b5.df_files_MBTI_colleague_match_.rename(columns = {'Openness':'OPE', 'Conscientiousness':'CON', 'Extraversion': 'EXT', 'Agreeableness': 'AGR', 'Non-Neuroticism': 'NNEU'})\n",
    "columns_to_round = df.columns[1:6]\n",
    "df[columns_to_round] = df[columns_to_round].apply(lambda x: [round(i, 3) for i in x])\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "eeddf596",
   "metadata": {},
   "source": [
    "<hr>\n",
    "\n",
    "Для определения степени выраженности персональных растройств необходимо знать коэффициенты корреляции между персональными качествами личности человека и типами личности MBTI, а также кооэффициенты корреляции между типами личности MBTI и персональными растройствами.\n",
    "\n",
    "В качестве примера предлагается использование коэффициентов корреляции между персональными качествами личности человека и типами личности MBTI, представленных в статье [1] и кооэффициентов корреляции между типами личности MBTI и персональными растройствами [2].\n",
    "\n",
    "1) Furnham A. The big five facets and the MBTI: The relationship between the 30 NEO-PI (R) Facets and the four Myers-Briggs Type Indicator (MBTI) scores // Psychology. - 2022. vol. 13(10). - pp. 1504-1516.\n",
    "2) Furnham A. MBTI and aberrant personality traits: dark-side trait correlates of the Myers Briggs type inventory // Psychology. - 2022. - vol. 13(5). - pp 805-815.\n",
    "\n",
    "Среди персональных расстройста рассматриваются следующие:\n",
    "1) Параноидное (Paranoid) — Недоверие и подозрительность по отношению к другим; мотивы интерпретируются как злонамеренные.\n",
    "2) Шизоидное (Schizoid) — Эмоциональная холодность и отстранённость от социальных отношений; безразличие к похвалам и критике.\n",
    "3) Шизотипическое (Schizotypal) — Странные убеждения или магическое мышление; поведение или речь, которые кажутся странными, эксцентричными или необычными.\n",
    "4) Антисоциальное (Antisocial) — Пренебрежение к истине; импульсивность и неспособность планировать будущее; нарушение социальных норм.\n",
    "5) Пограничное (Borderline) — Неуместная злость; нестабильные и интенсивные отношения, которые чередуются между идеализацией и обесцениванием.\n",
    "6) Истерическое (Histrionic) — Чрезмерная эмоциональность и стремление к вниманию; драматизация, театральное поведение и преувеличенное выражение эмоций.\n",
    "7) Нарциссическое (Narcissistic) — Высокомерные и надменные манеры или установки, преувеличенное чувство собственной значимости и права на особое отношение.\n",
    "8) Избегающее (Avoidant) — Социальное избегание; чувство неполноценности и повышенная чувствительность к критике или отказу.\n",
    "9) Зависимое (Dependent) — Трудности в принятии повседневных решений без чрезмерных советов и уверений; трудности в выражении несогласия из-за страха потери поддержки или одобрения.\n",
    "10) Обсессивно-компульсивное личностное расстройство (OCPD) — Чрезмерная озабоченность порядком, перфекционизмом, контролем и деталями; стремление к соблюдению правил, часто в ущерб гибкости и эффективности.\n",
    "\n",
    "\n",
    "Пользователь может установить свои коэффициенты корреляции"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "5b2cb179",
   "metadata": {},
   "outputs": [],
   "source": [
    "url = 'https://download.sberdisk.ru/download/file/493644095?token=EX7hFxNJhMoLumI&filename=df_mbti_correlation.csv'\n",
    "df_correlation_coefficients_mbti = pd.read_csv(url)\n",
    "\n",
    "df_correlation_coefficients_mbti.index.name = 'ID'\n",
    "df_correlation_coefficients_mbti.index += 1\n",
    "df_correlation_coefficients_mbti.index = df_correlation_coefficients_mbti.index.map(str)\n",
    "\n",
    "url = 'https://download.sberdisk.ru/download/file/493644096?token=T309xfzRosPj3v9&filename=df_disorder_correlation.csv'\n",
    "df_correlation_coefficients_disorders = pd.read_csv(url)\n",
    "\n",
    "df_correlation_coefficients_disorders.index.name = 'ID'\n",
    "df_correlation_coefficients_disorders.index += 1\n",
    "df_correlation_coefficients_disorders.index = df_correlation_coefficients_disorders.index.map(str)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "e4096ffc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Path</th>\n",
       "      <th>OPE</th>\n",
       "      <th>CON</th>\n",
       "      <th>EXT</th>\n",
       "      <th>AGR</th>\n",
       "      <th>NNEU</th>\n",
       "      <th>MBTI</th>\n",
       "      <th>Disorder 1</th>\n",
       "      <th>Disorder 2</th>\n",
       "      <th>Disorder 3</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Person ID</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>speaker_01_center_83.mov</td>\n",
       "      <td>0.766</td>\n",
       "      <td>0.697</td>\n",
       "      <td>0.656</td>\n",
       "      <td>0.760</td>\n",
       "      <td>0.494</td>\n",
       "      <td>ENFP</td>\n",
       "      <td>Narcissistic (0.121)</td>\n",
       "      <td>Paranoid (0.119)</td>\n",
       "      <td>Dependent (0.083)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>speaker_06_center_83.mov</td>\n",
       "      <td>0.687</td>\n",
       "      <td>0.659</td>\n",
       "      <td>0.612</td>\n",
       "      <td>0.750</td>\n",
       "      <td>0.421</td>\n",
       "      <td>ENFP</td>\n",
       "      <td>Narcissistic (0.114)</td>\n",
       "      <td>Paranoid (0.112)</td>\n",
       "      <td>Dependent (0.078)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>speaker_07_center_83.mov</td>\n",
       "      <td>0.672</td>\n",
       "      <td>0.661</td>\n",
       "      <td>0.572</td>\n",
       "      <td>0.705</td>\n",
       "      <td>0.381</td>\n",
       "      <td>ENFP</td>\n",
       "      <td>Narcissistic (0.105)</td>\n",
       "      <td>Paranoid (0.104)</td>\n",
       "      <td>Dependent (0.071)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>speaker_10_center_83.mov</td>\n",
       "      <td>0.698</td>\n",
       "      <td>0.599</td>\n",
       "      <td>0.572</td>\n",
       "      <td>0.675</td>\n",
       "      <td>0.351</td>\n",
       "      <td>ENFP</td>\n",
       "      <td>Narcissistic (0.106)</td>\n",
       "      <td>Paranoid (0.105)</td>\n",
       "      <td>Dependent (0.071)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>speaker_11_center_83.mov</td>\n",
       "      <td>0.718</td>\n",
       "      <td>0.599</td>\n",
       "      <td>0.574</td>\n",
       "      <td>0.732</td>\n",
       "      <td>0.380</td>\n",
       "      <td>ENFP</td>\n",
       "      <td>Narcissistic (0.115)</td>\n",
       "      <td>Paranoid (0.113)</td>\n",
       "      <td>Dependent (0.079)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>speaker_15_center_83.mov</td>\n",
       "      <td>0.671</td>\n",
       "      <td>0.671</td>\n",
       "      <td>0.602</td>\n",
       "      <td>0.709</td>\n",
       "      <td>0.400</td>\n",
       "      <td>ENFP</td>\n",
       "      <td>Narcissistic (0.107)</td>\n",
       "      <td>Paranoid (0.105)</td>\n",
       "      <td>Dependent (0.072)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>speaker_19_center_83.mov</td>\n",
       "      <td>0.767</td>\n",
       "      <td>0.658</td>\n",
       "      <td>0.653</td>\n",
       "      <td>0.801</td>\n",
       "      <td>0.463</td>\n",
       "      <td>ENFP</td>\n",
       "      <td>Narcissistic (0.127)</td>\n",
       "      <td>Paranoid (0.125)</td>\n",
       "      <td>Dependent (0.087)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>speaker_23_center_83.mov</td>\n",
       "      <td>0.700</td>\n",
       "      <td>0.685</td>\n",
       "      <td>0.617</td>\n",
       "      <td>0.806</td>\n",
       "      <td>0.448</td>\n",
       "      <td>ENFP</td>\n",
       "      <td>Narcissistic (0.12)</td>\n",
       "      <td>Paranoid (0.118)</td>\n",
       "      <td>Dependent (0.083)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>speaker_24_center_83.mov</td>\n",
       "      <td>0.711</td>\n",
       "      <td>0.663</td>\n",
       "      <td>0.611</td>\n",
       "      <td>0.711</td>\n",
       "      <td>0.414</td>\n",
       "      <td>ENFP</td>\n",
       "      <td>Narcissistic (0.11)</td>\n",
       "      <td>Paranoid (0.109)</td>\n",
       "      <td>Dependent (0.074)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>speaker_27_center_83.mov</td>\n",
       "      <td>0.759</td>\n",
       "      <td>0.713</td>\n",
       "      <td>0.658</td>\n",
       "      <td>0.831</td>\n",
       "      <td>0.508</td>\n",
       "      <td>ENFP</td>\n",
       "      <td>Paranoid (0.09)</td>\n",
       "      <td>Narcissistic (0.088)</td>\n",
       "      <td>Histrionic (0.073)</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                               Path    OPE    CON    EXT    AGR   NNEU  MBTI  \\\n",
       "Person ID                                                                      \n",
       "1          speaker_01_center_83.mov  0.766  0.697  0.656  0.760  0.494  ENFP   \n",
       "2          speaker_06_center_83.mov  0.687  0.659  0.612  0.750  0.421  ENFP   \n",
       "3          speaker_07_center_83.mov  0.672  0.661  0.572  0.705  0.381  ENFP   \n",
       "4          speaker_10_center_83.mov  0.698  0.599  0.572  0.675  0.351  ENFP   \n",
       "5          speaker_11_center_83.mov  0.718  0.599  0.574  0.732  0.380  ENFP   \n",
       "6          speaker_15_center_83.mov  0.671  0.671  0.602  0.709  0.400  ENFP   \n",
       "7          speaker_19_center_83.mov  0.767  0.658  0.653  0.801  0.463  ENFP   \n",
       "8          speaker_23_center_83.mov  0.700  0.685  0.617  0.806  0.448  ENFP   \n",
       "9          speaker_24_center_83.mov  0.711  0.663  0.611  0.711  0.414  ENFP   \n",
       "10         speaker_27_center_83.mov  0.759  0.713  0.658  0.831  0.508  ENFP   \n",
       "\n",
       "                     Disorder 1            Disorder 2          Disorder 3  \n",
       "Person ID                                                                  \n",
       "1          Narcissistic (0.121)      Paranoid (0.119)   Dependent (0.083)  \n",
       "2          Narcissistic (0.114)      Paranoid (0.112)   Dependent (0.078)  \n",
       "3          Narcissistic (0.105)      Paranoid (0.104)   Dependent (0.071)  \n",
       "4          Narcissistic (0.106)      Paranoid (0.105)   Dependent (0.071)  \n",
       "5          Narcissistic (0.115)      Paranoid (0.113)   Dependent (0.079)  \n",
       "6          Narcissistic (0.107)      Paranoid (0.105)   Dependent (0.072)  \n",
       "7          Narcissistic (0.127)      Paranoid (0.125)   Dependent (0.087)  \n",
       "8           Narcissistic (0.12)      Paranoid (0.118)   Dependent (0.083)  \n",
       "9           Narcissistic (0.11)      Paranoid (0.109)   Dependent (0.074)  \n",
       "10              Paranoid (0.09)  Narcissistic (0.088)  Histrionic (0.073)  "
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "_b5._colleague_personality_desorders(\n",
    "    correlation_coefficients_mbti = df_correlation_coefficients_mbti,\n",
    "    correlation_coefficients_disorders = df_correlation_coefficients_disorders,\n",
    "    personality_desorder_number = 3,\n",
    "    threshold = 0.5,\n",
    "    out = True\n",
    ")\n",
    "\n",
    "_b5._save_logs(df = _b5.df_files_MBTI_disorders_, name = 'MBTI_colleague_personality_type_match_fi_en', out = True)\n",
    "\n",
    "# Optional\n",
    "df = _b5.df_files_MBTI_disorders_.rename(columns = {'Openness':'OPE', 'Conscientiousness':'CON', 'Extraversion': 'EXT', 'Agreeableness': 'AGR', 'Non-Neuroticism': 'NNEU'})\n",
    "columns_to_round = df.columns[1:6]\n",
    "df[columns_to_round] = df[columns_to_round].apply(lambda x: [round(i, 3) for i in x])\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7d55e24a-6afc-4a9c-8a2d-b459c4927225",
   "metadata": {},
   "source": [
    "### `MuPTA` (en)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "6aad9dfa-d10b-46f5-b40b-18c142dbfc25",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/markdown": [
       "<span style=\"color:#666\">**[</span><span style=\"color:#1776D2\">2024-10-10 22:04:06</span><span style=\"color:#666\">]</span> <span style=\"color:#666\">Извлечение признаков (экспертных и нейросетевых) из текста ...</span>** "
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "<span style=\"color:#666\">**[</span><span style=\"color:#1776D2\">2024-10-10 22:04:06</span><span style=\"color:#666\">]</span> <span style=\"color:#666\">Получение прогнозов и вычисление точности (мультимодальное объединение) ...</span>**<p><span style=\"color:#666\">&nbsp;&nbsp;&nbsp;&nbsp;10 из 10 (100.0%) ... GitHub\\OCEANAI\\docs\\source\\user_guide\\notebooks\\video_MuPTA\\test\\speaker_27_center_83.mov ...</span></p>"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
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       "    }\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>Path</th>\n",
       "      <th>Openness</th>\n",
       "      <th>Conscientiousness</th>\n",
       "      <th>Extraversion</th>\n",
       "      <th>Agreeableness</th>\n",
       "      <th>Non-Neuroticism</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Person ID</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>speaker_01_center_83.mov</td>\n",
       "      <td>0.59561</td>\n",
       "      <td>0.542967</td>\n",
       "      <td>0.440668</td>\n",
       "      <td>0.589769</td>\n",
       "      <td>0.515306</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>speaker_06_center_83.mov</td>\n",
       "      <td>0.661347</td>\n",
       "      <td>0.673973</td>\n",
       "      <td>0.603208</td>\n",
       "      <td>0.64543</td>\n",
       "      <td>0.6431</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>speaker_07_center_83.mov</td>\n",
       "      <td>0.439868</td>\n",
       "      <td>0.465049</td>\n",
       "      <td>0.284547</td>\n",
       "      <td>0.422551</td>\n",
       "      <td>0.396058</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>speaker_10_center_83.mov</td>\n",
       "      <td>0.47715</td>\n",
       "      <td>0.502563</td>\n",
       "      <td>0.373686</td>\n",
       "      <td>0.441372</td>\n",
       "      <td>0.424637</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>speaker_11_center_83.mov</td>\n",
       "      <td>0.403292</td>\n",
       "      <td>0.344359</td>\n",
       "      <td>0.317304</td>\n",
       "      <td>0.422228</td>\n",
       "      <td>0.384346</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>speaker_15_center_83.mov</td>\n",
       "      <td>0.581837</td>\n",
       "      <td>0.562177</td>\n",
       "      <td>0.504623</td>\n",
       "      <td>0.602169</td>\n",
       "      <td>0.522254</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>speaker_19_center_83.mov</td>\n",
       "      <td>0.510444</td>\n",
       "      <td>0.448468</td>\n",
       "      <td>0.425599</td>\n",
       "      <td>0.451861</td>\n",
       "      <td>0.447891</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>speaker_23_center_83.mov</td>\n",
       "      <td>0.500526</td>\n",
       "      <td>0.541376</td>\n",
       "      <td>0.308529</td>\n",
       "      <td>0.441178</td>\n",
       "      <td>0.452412</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>speaker_24_center_83.mov</td>\n",
       "      <td>0.427677</td>\n",
       "      <td>0.511355</td>\n",
       "      <td>0.301078</td>\n",
       "      <td>0.434281</td>\n",
       "      <td>0.442301</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>speaker_27_center_83.mov</td>\n",
       "      <td>0.566414</td>\n",
       "      <td>0.659169</td>\n",
       "      <td>0.434059</td>\n",
       "      <td>0.59122</td>\n",
       "      <td>0.579172</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                               Path  Openness Conscientiousness Extraversion  \\\n",
       "Person ID                                                                      \n",
       "1          speaker_01_center_83.mov   0.59561          0.542967     0.440668   \n",
       "2          speaker_06_center_83.mov  0.661347          0.673973     0.603208   \n",
       "3          speaker_07_center_83.mov  0.439868          0.465049     0.284547   \n",
       "4          speaker_10_center_83.mov   0.47715          0.502563     0.373686   \n",
       "5          speaker_11_center_83.mov  0.403292          0.344359     0.317304   \n",
       "6          speaker_15_center_83.mov  0.581837          0.562177     0.504623   \n",
       "7          speaker_19_center_83.mov  0.510444          0.448468     0.425599   \n",
       "8          speaker_23_center_83.mov  0.500526          0.541376     0.308529   \n",
       "9          speaker_24_center_83.mov  0.427677          0.511355     0.301078   \n",
       "10         speaker_27_center_83.mov  0.566414          0.659169     0.434059   \n",
       "\n",
       "          Agreeableness Non-Neuroticism  \n",
       "Person ID                                \n",
       "1              0.589769        0.515306  \n",
       "2               0.64543          0.6431  \n",
       "3              0.422551        0.396058  \n",
       "4              0.441372        0.424637  \n",
       "5              0.422228        0.384346  \n",
       "6              0.602169        0.522254  \n",
       "7              0.451861        0.447891  \n",
       "8              0.441178        0.452412  \n",
       "9              0.434281        0.442301  \n",
       "10              0.59122        0.579172  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "<span style=\"color:#666\">**[</span><span style=\"color:#1776D2\">2024-10-10 22:04:06</span><span style=\"color:#666\">]</span> <span style=\"color:#666\">Точность по отдельным персональным качествам личности человека ...</span>** "
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Openness</th>\n",
       "      <th>Conscientiousness</th>\n",
       "      <th>Extraversion</th>\n",
       "      <th>Agreeableness</th>\n",
       "      <th>Non-Neuroticism</th>\n",
       "      <th>Mean</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Metrics</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>MAE</th>\n",
       "      <td>0.1632</td>\n",
       "      <td>0.1621</td>\n",
       "      <td>0.176</td>\n",
       "      <td>0.2589</td>\n",
       "      <td>0.1122</td>\n",
       "      <td>0.1745</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Accuracy</th>\n",
       "      <td>0.8368</td>\n",
       "      <td>0.8379</td>\n",
       "      <td>0.824</td>\n",
       "      <td>0.7411</td>\n",
       "      <td>0.8878</td>\n",
       "      <td>0.8255</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          Openness  Conscientiousness  Extraversion  Agreeableness  \\\n",
       "Metrics                                                              \n",
       "MAE         0.1632             0.1621         0.176         0.2589   \n",
       "Accuracy    0.8368             0.8379         0.824         0.7411   \n",
       "\n",
       "          Non-Neuroticism    Mean  \n",
       "Metrics                            \n",
       "MAE                0.1122  0.1745  \n",
       "Accuracy           0.8878  0.8255  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "<span style=\"color:#666\">**[</span><span style=\"color:#1776D2\">2024-10-10 22:04:06</span><span style=\"color:#666\">]</span> <span style=\"color:#666\">Средняя средних абсолютных ошибок: <span style=\"color:#1776D2\">0.1745</span>, средняя точность: <span style=\"color:#1776D2\">0.8255</span> ...</span>** "
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "<span style=\"color:#008001\">**Лог файлы успешно сохранены ...**</span>"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "<span style=\"color:#666\">**--- Время выполнения: 302.368 сек. ---**</span>"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import os\n",
    "import pandas as pd\n",
    "\n",
    "# Импорт модуля\n",
    "from oceanai.modules.lab.build import Run\n",
    "\n",
    "# Создание экземпляра класса\n",
    "_b5 = Run()\n",
    "\n",
    "corpus = 'fi'\n",
    "lang = 'en'\n",
    "\n",
    "# Настройка ядра\n",
    "_b5.path_to_save_ = './models' # Директория для сохранения файла\n",
    "_b5.chunk_size_ = 2000000      # Размер загрузки файла из сети за 1 шаг\n",
    "\n",
    "# Формирование аудиомоделей\n",
    "res_load_model_hc = _b5.load_audio_model_hc()\n",
    "res_load_model_nn = _b5.load_audio_model_nn()\n",
    "\n",
    "# Загрузка весов аудиомоделей\n",
    "url = _b5.weights_for_big5_['audio'][corpus]['hc']['googledisk']\n",
    "res_load_model_weights_hc = _b5.load_audio_model_weights_hc(url = url, force_reload = False)\n",
    "\n",
    "url = _b5.weights_for_big5_['audio'][corpus]['nn']['googledisk']\n",
    "res_load_model_weights_nn = _b5.load_audio_model_weights_nn(url = url, force_reload = False)\n",
    "\n",
    "# Формирование видеомоделей\n",
    "res_load_model_hc = _b5.load_video_model_hc(lang=lang)\n",
    "res_load_model_deep_fe = _b5.load_video_model_deep_fe()\n",
    "res_load_model_nn = _b5.load_video_model_nn()\n",
    "\n",
    "# Загрузка весов видеомоделей\n",
    "url = _b5.weights_for_big5_['video'][corpus]['hc']['googledisk']\n",
    "res_load_model_weights_hc = _b5.load_video_model_weights_hc(url = url, force_reload = False)\n",
    "\n",
    "url = _b5.weights_for_big5_['video'][corpus]['fe']['googledisk']\n",
    "res_load_model_weights_deep_fe = _b5.load_video_model_weights_deep_fe(url = url, force_reload = False)\n",
    "\n",
    "url = _b5.weights_for_big5_['video'][corpus]['nn']['googledisk']\n",
    "res_load_model_weights_nn = _b5.load_video_model_weights_nn(url = url, force_reload = False)\n",
    "\n",
    "# Загрузка словаря с экспертными признаками (текстовая модальность)\n",
    "res_load_text_features = _b5.load_text_features()\n",
    "\n",
    "# Формирование текстовых моделей \n",
    "res_setup_translation_model = _b5.setup_translation_model() # только для русского языка\n",
    "res_setup_translation_model = _b5.setup_bert_encoder(force_reload = False)\n",
    "res_load_text_model_hc_fi = _b5.load_text_model_hc(corpus=corpus)\n",
    "res_load_text_model_nn_fi = _b5.load_text_model_nn(corpus=corpus)\n",
    "\n",
    "# Загрузка весов текстовых моделей\n",
    "url = _b5.weights_for_big5_['text'][corpus]['hc']['googledisk']\n",
    "res_load_text_model_weights_hc_fi = _b5.load_text_model_weights_hc(url = url, force_reload = False)\n",
    "\n",
    "url = _b5.weights_for_big5_['text'][corpus]['nn']['googledisk']\n",
    "res_load_text_model_weights_nn_fi = _b5.load_text_model_weights_nn(url = url, force_reload = False)\n",
    "\n",
    "# Формирование модели для мультимодального объединения информации\n",
    "res_load_avt_model_b5 = _b5.load_avt_model_b5()\n",
    "\n",
    "# Загрузка весов модели для мультимодального объединения информации\n",
    "url = _b5.weights_for_big5_['avt'][corpus]['b5']['googledisk']\n",
    "res_load_avt_model_weights_b5 = _b5.load_avt_model_weights_b5(url = url)\n",
    "\n",
    "PATH_TO_DIR = './video_MuPTA/'\n",
    "PATH_SAVE_VIDEO = './video_MuPTA/test/'\n",
    "\n",
    "_b5.path_to_save_ = PATH_SAVE_VIDEO\n",
    "\n",
    "# Загрузка 10 тестовых аудиовидеозаписей из корпуса MuPTA\n",
    "# URL: https://hci.nw.ru/en/pages/mupta-corpus\n",
    "domain = 'https://download.sberdisk.ru/download/file/'\n",
    "tets_name_files = [\n",
    "    '477995979?token=2cvyk7CS0mHx2MJ&filename=speaker_06_center_83.mov',\n",
    "    '477995980?token=jGPtBPS69uzFU6Y&filename=speaker_01_center_83.mov',\n",
    "    '477995967?token=zCaRbNB6ht5wMPq&filename=speaker_11_center_83.mov',\n",
    "    '477995966?token=B1rbinDYRQKrI3T&filename=speaker_15_center_83.mov',\n",
    "    '477995978?token=dEpVDtZg1EQiEQ9&filename=speaker_07_center_83.mov',\n",
    "    '477995961?token=o1hVjw8G45q9L9Z&filename=speaker_19_center_83.mov',\n",
    "    '477995964?token=5K220Aqf673VHPq&filename=speaker_23_center_83.mov',\n",
    "    '477995965?token=v1LVD2KT1cU7Lpb&filename=speaker_24_center_83.mov',\n",
    "    '477995962?token=tmaSGyyWLA6XCy9&filename=speaker_27_center_83.mov',\n",
    "    '477995963?token=bTpo96qNDPcwGqb&filename=speaker_10_center_83.mov',\n",
    "]\n",
    "\n",
    "for curr_files in tets_name_files:\n",
    "    _b5.download_file_from_url(url = domain + curr_files, out = True)\n",
    "\n",
    "# Получение прогнозов\n",
    "_b5.path_to_dataset_ = PATH_TO_DIR # Директория набора данных\n",
    "_b5.ext_ = ['.mov'] # Расширения искомых файлов\n",
    "\n",
    "# Полный путь к файлу с верными предсказаниями для подсчета точности\n",
    "url_accuracy = _b5.true_traits_['mupta']['googledisk']\n",
    "\n",
    "_b5.get_avt_predictions(url_accuracy = url_accuracy, lang = lang)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "023c2f19-1b4c-4a54-b003-625cd65b5f02",
   "metadata": {},
   "source": [
    "<hr>\n",
    "\n",
    "Для поиска подходящего коллеги по работе необходимо знать по два коэффициента корреляции для каждого персонального качества личности человека. Эти коэффициенты должны показывать, как изменится оценка качества одного человека, если она будет больше или меньше оценки качества другого человека.\n",
    "\n",
    "В качестве примера предлагается использование коэффициентов корреляции между двумя людьми в контексте отношений \"начальник-подчиненный\", представленных в статье:\n",
    "\n",
    "1) Kuroda S., Yamamoto I. Good boss, bad boss, workers’ mental health and productivity: Evidence from Japan // Japan & The World Economy. – 2018. – vol. 48. – pp. 106-118.\n",
    "\n",
    "Пользователь может установить свои коэффициенты корреляции"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "0bff6386-db26-4951-aa29-7ca448b9a53c",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Score_comparison</th>\n",
       "      <th>Openness</th>\n",
       "      <th>Conscientiousness</th>\n",
       "      <th>Extraversion</th>\n",
       "      <th>Agreeableness</th>\n",
       "      <th>Non-Neuroticism</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ID</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>higher</td>\n",
       "      <td>-0.0602</td>\n",
       "      <td>0.0471</td>\n",
       "      <td>-0.1070</td>\n",
       "      <td>-0.0832</td>\n",
       "      <td>0.190</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>lower</td>\n",
       "      <td>-0.1720</td>\n",
       "      <td>-0.1050</td>\n",
       "      <td>0.0772</td>\n",
       "      <td>0.0703</td>\n",
       "      <td>-0.229</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Score_comparison  Openness  Conscientiousness  Extraversion  Agreeableness  \\\n",
       "ID                                                                              \n",
       "1            higher   -0.0602             0.0471       -0.1070        -0.0832   \n",
       "2             lower   -0.1720            -0.1050        0.0772         0.0703   \n",
       "\n",
       "    Non-Neuroticism  \n",
       "ID                   \n",
       "1             0.190  \n",
       "2            -0.229  "
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Загрузка датафрейма с коэффициентами корреляции\n",
    "url = 'https://download.sberdisk.ru/download/file/478675819?token=LuB7L1QsEY0UuSs&filename=colleague_ranking.csv'\n",
    "df_correlation_coefficients = pd.read_csv(url)\n",
    "df_correlation_coefficients = pd.DataFrame(\n",
    "    df_correlation_coefficients.drop(['ID'], axis = 1)\n",
    ")\n",
    "\n",
    "df_correlation_coefficients.index.name = 'ID'\n",
    "df_correlation_coefficients.index += 1\n",
    "df_correlation_coefficients.index = df_correlation_coefficients.index.map(str)\n",
    "\n",
    "df_correlation_coefficients"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a9f9d35f-e855-4e20-91b4-8dc3544f2b19",
   "metadata": {},
   "source": [
    "#### Поиск старшего коллеги"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "2c885d73-d82e-4790-9d52-2e58f3d9d022",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Path</th>\n",
       "      <th>OPE</th>\n",
       "      <th>CON</th>\n",
       "      <th>EXT</th>\n",
       "      <th>AGR</th>\n",
       "      <th>NNEU</th>\n",
       "      <th>Match</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Person ID</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>speaker_27_center_83.mov</td>\n",
       "      <td>0.566</td>\n",
       "      <td>0.659</td>\n",
       "      <td>0.434</td>\n",
       "      <td>0.591</td>\n",
       "      <td>0.579</td>\n",
       "      <td>0.091</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>speaker_23_center_83.mov</td>\n",
       "      <td>0.501</td>\n",
       "      <td>0.541</td>\n",
       "      <td>0.309</td>\n",
       "      <td>0.441</td>\n",
       "      <td>0.452</td>\n",
       "      <td>0.080</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>speaker_01_center_83.mov</td>\n",
       "      <td>0.596</td>\n",
       "      <td>0.543</td>\n",
       "      <td>0.441</td>\n",
       "      <td>0.590</td>\n",
       "      <td>0.515</td>\n",
       "      <td>0.073</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>speaker_19_center_83.mov</td>\n",
       "      <td>0.510</td>\n",
       "      <td>0.448</td>\n",
       "      <td>0.426</td>\n",
       "      <td>0.452</td>\n",
       "      <td>0.448</td>\n",
       "      <td>0.015</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>speaker_06_center_83.mov</td>\n",
       "      <td>0.661</td>\n",
       "      <td>0.674</td>\n",
       "      <td>0.603</td>\n",
       "      <td>0.645</td>\n",
       "      <td>0.643</td>\n",
       "      <td>-0.004</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>speaker_15_center_83.mov</td>\n",
       "      <td>0.582</td>\n",
       "      <td>0.562</td>\n",
       "      <td>0.505</td>\n",
       "      <td>0.602</td>\n",
       "      <td>0.522</td>\n",
       "      <td>-0.013</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>speaker_11_center_83.mov</td>\n",
       "      <td>0.403</td>\n",
       "      <td>0.344</td>\n",
       "      <td>0.317</td>\n",
       "      <td>0.422</td>\n",
       "      <td>0.384</td>\n",
       "      <td>-0.139</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>speaker_07_center_83.mov</td>\n",
       "      <td>0.440</td>\n",
       "      <td>0.465</td>\n",
       "      <td>0.285</td>\n",
       "      <td>0.423</td>\n",
       "      <td>0.396</td>\n",
       "      <td>-0.164</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>speaker_10_center_83.mov</td>\n",
       "      <td>0.477</td>\n",
       "      <td>0.503</td>\n",
       "      <td>0.374</td>\n",
       "      <td>0.441</td>\n",
       "      <td>0.425</td>\n",
       "      <td>-0.172</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>speaker_24_center_83.mov</td>\n",
       "      <td>0.428</td>\n",
       "      <td>0.511</td>\n",
       "      <td>0.301</td>\n",
       "      <td>0.434</td>\n",
       "      <td>0.442</td>\n",
       "      <td>-0.175</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                               Path    OPE    CON    EXT    AGR   NNEU  Match\n",
       "Person ID                                                                    \n",
       "10         speaker_27_center_83.mov  0.566  0.659  0.434  0.591  0.579  0.091\n",
       "8          speaker_23_center_83.mov  0.501  0.541  0.309  0.441  0.452  0.080\n",
       "1          speaker_01_center_83.mov  0.596  0.543  0.441  0.590  0.515  0.073\n",
       "7          speaker_19_center_83.mov  0.510  0.448  0.426  0.452  0.448  0.015\n",
       "2          speaker_06_center_83.mov  0.661  0.674  0.603  0.645  0.643 -0.004\n",
       "6          speaker_15_center_83.mov  0.582  0.562  0.505  0.602  0.522 -0.013\n",
       "5          speaker_11_center_83.mov  0.403  0.344  0.317  0.422  0.384 -0.139\n",
       "3          speaker_07_center_83.mov  0.440  0.465  0.285  0.423  0.396 -0.164\n",
       "4          speaker_10_center_83.mov  0.477  0.503  0.374  0.441  0.425 -0.172\n",
       "9          speaker_24_center_83.mov  0.428  0.511  0.301  0.434  0.442 -0.175"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Список оценок персональных качеств личности целевого человека\n",
    "target_scores = [0.527886, 0.522337, 0.458468, 0.51761, 0.444649]\n",
    "\n",
    "_b5._colleague_ranking(\n",
    "    correlation_coefficients = df_correlation_coefficients,\n",
    "    target_scores = target_scores,\n",
    "    colleague = 'major',\n",
    "    equal_coefficients = 0.5,\n",
    "    out = False\n",
    ")\n",
    "\n",
    "_b5._save_logs(df = _b5.df_files_colleague_, name = 'major_colleague_ranking_mupta_en', out = True)\n",
    "\n",
    "# Опционно\n",
    "df = _b5.df_files_colleague_.rename(columns = {'Openness':'OPE', 'Conscientiousness':'CON', 'Extraversion': 'EXT', 'Agreeableness': 'AGR', 'Non-Neuroticism': 'NNEU'})\n",
    "columns_to_round = df.columns[1:]\n",
    "df[columns_to_round] = df[columns_to_round].apply(lambda x: [round(i, 3) for i in x])\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "51bc4b65-d97b-4f01-86bd-96492992908b",
   "metadata": {},
   "source": [
    "#### Поиск младшего коллеги"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "d6b2a45c-f103-4802-bbef-1da6fce784fc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Path</th>\n",
       "      <th>OPE</th>\n",
       "      <th>CON</th>\n",
       "      <th>EXT</th>\n",
       "      <th>AGR</th>\n",
       "      <th>NNEU</th>\n",
       "      <th>Match</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Person ID</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>speaker_24_center_83.mov</td>\n",
       "      <td>0.428</td>\n",
       "      <td>0.511</td>\n",
       "      <td>0.301</td>\n",
       "      <td>0.434</td>\n",
       "      <td>0.442</td>\n",
       "      <td>0.014</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>speaker_07_center_83.mov</td>\n",
       "      <td>0.440</td>\n",
       "      <td>0.465</td>\n",
       "      <td>0.285</td>\n",
       "      <td>0.423</td>\n",
       "      <td>0.396</td>\n",
       "      <td>0.005</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>speaker_10_center_83.mov</td>\n",
       "      <td>0.477</td>\n",
       "      <td>0.503</td>\n",
       "      <td>0.374</td>\n",
       "      <td>0.441</td>\n",
       "      <td>0.425</td>\n",
       "      <td>-0.001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>speaker_11_center_83.mov</td>\n",
       "      <td>0.403</td>\n",
       "      <td>0.344</td>\n",
       "      <td>0.317</td>\n",
       "      <td>0.422</td>\n",
       "      <td>0.384</td>\n",
       "      <td>-0.004</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>speaker_19_center_83.mov</td>\n",
       "      <td>0.510</td>\n",
       "      <td>0.448</td>\n",
       "      <td>0.426</td>\n",
       "      <td>0.452</td>\n",
       "      <td>0.448</td>\n",
       "      <td>-0.195</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>speaker_15_center_83.mov</td>\n",
       "      <td>0.582</td>\n",
       "      <td>0.562</td>\n",
       "      <td>0.505</td>\n",
       "      <td>0.602</td>\n",
       "      <td>0.522</td>\n",
       "      <td>-0.198</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>speaker_06_center_83.mov</td>\n",
       "      <td>0.661</td>\n",
       "      <td>0.674</td>\n",
       "      <td>0.603</td>\n",
       "      <td>0.645</td>\n",
       "      <td>0.643</td>\n",
       "      <td>-0.240</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>speaker_23_center_83.mov</td>\n",
       "      <td>0.501</td>\n",
       "      <td>0.541</td>\n",
       "      <td>0.309</td>\n",
       "      <td>0.441</td>\n",
       "      <td>0.452</td>\n",
       "      <td>-0.260</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>speaker_01_center_83.mov</td>\n",
       "      <td>0.596</td>\n",
       "      <td>0.543</td>\n",
       "      <td>0.441</td>\n",
       "      <td>0.590</td>\n",
       "      <td>0.515</td>\n",
       "      <td>-0.283</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>speaker_27_center_83.mov</td>\n",
       "      <td>0.566</td>\n",
       "      <td>0.659</td>\n",
       "      <td>0.434</td>\n",
       "      <td>0.591</td>\n",
       "      <td>0.579</td>\n",
       "      <td>-0.304</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                               Path    OPE    CON    EXT    AGR   NNEU  Match\n",
       "Person ID                                                                    \n",
       "9          speaker_24_center_83.mov  0.428  0.511  0.301  0.434  0.442  0.014\n",
       "3          speaker_07_center_83.mov  0.440  0.465  0.285  0.423  0.396  0.005\n",
       "4          speaker_10_center_83.mov  0.477  0.503  0.374  0.441  0.425 -0.001\n",
       "5          speaker_11_center_83.mov  0.403  0.344  0.317  0.422  0.384 -0.004\n",
       "7          speaker_19_center_83.mov  0.510  0.448  0.426  0.452  0.448 -0.195\n",
       "6          speaker_15_center_83.mov  0.582  0.562  0.505  0.602  0.522 -0.198\n",
       "2          speaker_06_center_83.mov  0.661  0.674  0.603  0.645  0.643 -0.240\n",
       "8          speaker_23_center_83.mov  0.501  0.541  0.309  0.441  0.452 -0.260\n",
       "1          speaker_01_center_83.mov  0.596  0.543  0.441  0.590  0.515 -0.283\n",
       "10         speaker_27_center_83.mov  0.566  0.659  0.434  0.591  0.579 -0.304"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Список оценок персональных качеств личности целевого человека\n",
    "target_scores = [0.527886, 0.522337, 0.458468, 0.51761, 0.444649]\n",
    "\n",
    "_b5._colleague_ranking(\n",
    "    correlation_coefficients = df_correlation_coefficients,\n",
    "    target_scores = target_scores,\n",
    "    colleague = 'minor',\n",
    "    equal_coefficients = 0.5,\n",
    "    out = False\n",
    ")\n",
    "\n",
    "_b5._save_logs(df = _b5.df_files_colleague_, name = 'minor_colleague_ranking_mupta_en', out = True)\n",
    "\n",
    "# Опционно\n",
    "df = _b5.df_files_colleague_.rename(columns = {'Openness':'OPE', 'Conscientiousness':'CON', 'Extraversion': 'EXT', 'Agreeableness': 'AGR', 'Non-Neuroticism': 'NNEU'})\n",
    "columns_to_round = df.columns[1:]\n",
    "df[columns_to_round] = df[columns_to_round].apply(lambda x: [round(i, 3) for i in x])\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a620da41",
   "metadata": {},
   "source": [
    "<hr>\n",
    "\n",
    "Для поиска подходящего коллеги по типу личности MBTI необходимо знать коэффициенты корреляции между личностными качествами человека и типами личности MBTI, а также оценки этих качеств для целевого человека.\n",
    "\n",
    "В качестве примера предлагается использование коэффициентов корреляции, представленных в статье:\n",
    "\n",
    "1) Furnham A. The big five facets and the MBTI: The relationship between the 30 NEO-PI (R) Facets and the four Myers-Briggs Type Indicator (MBTI) scores // Psychology. - 2022. vol. 13(10). - pp. 1504-1516.\n",
    "\n",
    "Пользователь может установить свои коэффициенты корреляции"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "f983123f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Trait</th>\n",
       "      <th>EI</th>\n",
       "      <th>SN</th>\n",
       "      <th>TF</th>\n",
       "      <th>JP</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ID</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Openness</td>\n",
       "      <td>0.09</td>\n",
       "      <td>-0.03</td>\n",
       "      <td>-0.14</td>\n",
       "      <td>-0.16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Conscientiousness</td>\n",
       "      <td>0.04</td>\n",
       "      <td>-0.04</td>\n",
       "      <td>0.20</td>\n",
       "      <td>0.14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Extraversion</td>\n",
       "      <td>0.20</td>\n",
       "      <td>-0.03</td>\n",
       "      <td>0.01</td>\n",
       "      <td>-0.07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Agreeableness</td>\n",
       "      <td>0.02</td>\n",
       "      <td>0.05</td>\n",
       "      <td>-0.35</td>\n",
       "      <td>0.03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Non-Neuroticism</td>\n",
       "      <td>0.08</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.16</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                Trait    EI    SN    TF    JP\n",
       "ID                                           \n",
       "1            Openness  0.09 -0.03 -0.14 -0.16\n",
       "2   Conscientiousness  0.04 -0.04  0.20  0.14\n",
       "3        Extraversion  0.20 -0.03  0.01 -0.07\n",
       "4       Agreeableness  0.02  0.05 -0.35  0.03\n",
       "5     Non-Neuroticism  0.08  0.00  0.16  0.00"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Загрузка датафрейма с коэффициентами корреляции\n",
    "url = 'https://download.sberdisk.ru/download/file/493644095?token=EX7hFxNJhMoLumI&filename=df_mbti_correlation.csv'\n",
    "df_correlation_coefficients = pd.read_csv(url)\n",
    "\n",
    "df_correlation_coefficients.index.name = 'ID'\n",
    "df_correlation_coefficients.index += 1\n",
    "df_correlation_coefficients.index = df_correlation_coefficients.index.map(str)\n",
    "\n",
    "df_correlation_coefficients"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "adb4fe3b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Path</th>\n",
       "      <th>OPE</th>\n",
       "      <th>CON</th>\n",
       "      <th>EXT</th>\n",
       "      <th>AGR</th>\n",
       "      <th>NNEU</th>\n",
       "      <th>EI</th>\n",
       "      <th>SN</th>\n",
       "      <th>TF</th>\n",
       "      <th>JP</th>\n",
       "      <th>MBTI</th>\n",
       "      <th>Match</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Person ID</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>speaker_07_center_83.mov</td>\n",
       "      <td>0.440</td>\n",
       "      <td>0.465</td>\n",
       "      <td>0.285</td>\n",
       "      <td>0.423</td>\n",
       "      <td>0.396</td>\n",
       "      <td>-0.155235</td>\n",
       "      <td>0.019207</td>\n",
       "      <td>0.050250</td>\n",
       "      <td>0.012514</td>\n",
       "      <td>ISTJ</td>\n",
       "      <td>100.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>speaker_11_center_83.mov</td>\n",
       "      <td>0.403</td>\n",
       "      <td>0.344</td>\n",
       "      <td>0.317</td>\n",
       "      <td>0.422</td>\n",
       "      <td>0.384</td>\n",
       "      <td>-0.152724</td>\n",
       "      <td>0.014281</td>\n",
       "      <td>0.070700</td>\n",
       "      <td>0.025861</td>\n",
       "      <td>ISTJ</td>\n",
       "      <td>100.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>speaker_10_center_83.mov</td>\n",
       "      <td>0.477</td>\n",
       "      <td>0.503</td>\n",
       "      <td>0.374</td>\n",
       "      <td>0.441</td>\n",
       "      <td>0.425</td>\n",
       "      <td>-0.140376</td>\n",
       "      <td>-0.016646</td>\n",
       "      <td>0.250115</td>\n",
       "      <td>0.159620</td>\n",
       "      <td>INTJ</td>\n",
       "      <td>75.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>speaker_23_center_83.mov</td>\n",
       "      <td>0.501</td>\n",
       "      <td>0.541</td>\n",
       "      <td>0.309</td>\n",
       "      <td>0.441</td>\n",
       "      <td>0.452</td>\n",
       "      <td>-0.040020</td>\n",
       "      <td>-0.049474</td>\n",
       "      <td>0.117143</td>\n",
       "      <td>0.004070</td>\n",
       "      <td>INTJ</td>\n",
       "      <td>75.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>speaker_24_center_83.mov</td>\n",
       "      <td>0.428</td>\n",
       "      <td>0.511</td>\n",
       "      <td>0.301</td>\n",
       "      <td>0.434</td>\n",
       "      <td>0.442</td>\n",
       "      <td>-0.122322</td>\n",
       "      <td>-0.020306</td>\n",
       "      <td>0.240365</td>\n",
       "      <td>0.148065</td>\n",
       "      <td>INTJ</td>\n",
       "      <td>75.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>speaker_01_center_83.mov</td>\n",
       "      <td>0.596</td>\n",
       "      <td>0.543</td>\n",
       "      <td>0.441</td>\n",
       "      <td>0.590</td>\n",
       "      <td>0.515</td>\n",
       "      <td>0.040210</td>\n",
       "      <td>0.003122</td>\n",
       "      <td>-0.103169</td>\n",
       "      <td>0.029258</td>\n",
       "      <td>ESFJ</td>\n",
       "      <td>50.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>speaker_19_center_83.mov</td>\n",
       "      <td>0.510</td>\n",
       "      <td>0.448</td>\n",
       "      <td>0.426</td>\n",
       "      <td>0.452</td>\n",
       "      <td>0.448</td>\n",
       "      <td>-0.101987</td>\n",
       "      <td>-0.007200</td>\n",
       "      <td>-0.078923</td>\n",
       "      <td>-0.128220</td>\n",
       "      <td>INFP</td>\n",
       "      <td>25.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>speaker_27_center_83.mov</td>\n",
       "      <td>0.566</td>\n",
       "      <td>0.659</td>\n",
       "      <td>0.434</td>\n",
       "      <td>0.591</td>\n",
       "      <td>0.579</td>\n",
       "      <td>0.048690</td>\n",
       "      <td>-0.000776</td>\n",
       "      <td>-0.066064</td>\n",
       "      <td>0.049778</td>\n",
       "      <td>ENFJ</td>\n",
       "      <td>25.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>speaker_06_center_83.mov</td>\n",
       "      <td>0.661</td>\n",
       "      <td>0.674</td>\n",
       "      <td>0.603</td>\n",
       "      <td>0.645</td>\n",
       "      <td>0.643</td>\n",
       "      <td>0.271478</td>\n",
       "      <td>-0.032624</td>\n",
       "      <td>-0.074766</td>\n",
       "      <td>-0.034321</td>\n",
       "      <td>ENFP</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>speaker_15_center_83.mov</td>\n",
       "      <td>0.582</td>\n",
       "      <td>0.562</td>\n",
       "      <td>0.505</td>\n",
       "      <td>0.602</td>\n",
       "      <td>0.522</td>\n",
       "      <td>0.229601</td>\n",
       "      <td>-0.024972</td>\n",
       "      <td>-0.091174</td>\n",
       "      <td>-0.031648</td>\n",
       "      <td>ENFP</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                               Path    OPE    CON    EXT    AGR   NNEU  \\\n",
       "Person ID                                                                \n",
       "3          speaker_07_center_83.mov  0.440  0.465  0.285  0.423  0.396   \n",
       "5          speaker_11_center_83.mov  0.403  0.344  0.317  0.422  0.384   \n",
       "4          speaker_10_center_83.mov  0.477  0.503  0.374  0.441  0.425   \n",
       "8          speaker_23_center_83.mov  0.501  0.541  0.309  0.441  0.452   \n",
       "9          speaker_24_center_83.mov  0.428  0.511  0.301  0.434  0.442   \n",
       "1          speaker_01_center_83.mov  0.596  0.543  0.441  0.590  0.515   \n",
       "7          speaker_19_center_83.mov  0.510  0.448  0.426  0.452  0.448   \n",
       "10         speaker_27_center_83.mov  0.566  0.659  0.434  0.591  0.579   \n",
       "2          speaker_06_center_83.mov  0.661  0.674  0.603  0.645  0.643   \n",
       "6          speaker_15_center_83.mov  0.582  0.562  0.505  0.602  0.522   \n",
       "\n",
       "                 EI        SN        TF        JP  MBTI  Match  \n",
       "Person ID                                                       \n",
       "3         -0.155235  0.019207  0.050250  0.012514  ISTJ  100.0  \n",
       "5         -0.152724  0.014281  0.070700  0.025861  ISTJ  100.0  \n",
       "4         -0.140376 -0.016646  0.250115  0.159620  INTJ   75.0  \n",
       "8         -0.040020 -0.049474  0.117143  0.004070  INTJ   75.0  \n",
       "9         -0.122322 -0.020306  0.240365  0.148065  INTJ   75.0  \n",
       "1          0.040210  0.003122 -0.103169  0.029258  ESFJ   50.0  \n",
       "7         -0.101987 -0.007200 -0.078923 -0.128220  INFP   25.0  \n",
       "10         0.048690 -0.000776 -0.066064  0.049778  ENFJ   25.0  \n",
       "2          0.271478 -0.032624 -0.074766 -0.034321  ENFP    0.0  \n",
       "6          0.229601 -0.024972 -0.091174 -0.031648  ENFP    0.0  "
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "_b5._colleague_personality_type_match(\n",
    "    correlation_coefficients = df_correlation_coefficients,\n",
    "    target_scores = [0.34, 0.56, 0.42, 0.57, 0.56],\n",
    "    threshold = 0.5,\n",
    "    out = False\n",
    ")\n",
    "\n",
    "_b5._save_logs(df = _b5.df_files_MBTI_colleague_match_, name = 'MBTI_colleague_personality_type_match_en', out = True)\n",
    "\n",
    "# Optional\n",
    "df = _b5.df_files_MBTI_colleague_match_.rename(columns = {'Openness':'OPE', 'Conscientiousness':'CON', 'Extraversion': 'EXT', 'Agreeableness': 'AGR', 'Non-Neuroticism': 'NNEU'})\n",
    "columns_to_round = df.columns[1:6]\n",
    "df[columns_to_round] = df[columns_to_round].apply(lambda x: [round(i, 3) for i in x])\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b828c953",
   "metadata": {},
   "source": [
    "<hr>\n",
    "\n",
    "Для определения степени выраженности персональных растройств необходимо знать коэффициенты корреляции между персональными качествами личности человека и типами личности MBTI, а также кооэффициенты корреляции между типами личности MBTI и персональными растройствами.\n",
    "\n",
    "В качестве примера предлагается использование коэффициентов корреляции между персональными качествами личности человека и типами личности MBTI, представленных в статье [1] и кооэффициентов корреляции между типами личности MBTI и персональными растройствами [2].\n",
    "\n",
    "1) Furnham A. The big five facets and the MBTI: The relationship between the 30 NEO-PI (R) Facets and the four Myers-Briggs Type Indicator (MBTI) scores // Psychology. - 2022. vol. 13(10). - pp. 1504-1516.\n",
    "2) Furnham A. MBTI and aberrant personality traits: dark-side trait correlates of the Myers Briggs type inventory // Psychology. - 2022. - vol. 13(5). - pp 805-815.\n",
    "\n",
    "Среди персональных расстройста рассматриваются следующие:\n",
    "1) Параноидное (Paranoid) — Недоверие и подозрительность по отношению к другим; мотивы интерпретируются как злонамеренные.\n",
    "2) Шизоидное (Schizoid) — Эмоциональная холодность и отстранённость от социальных отношений; безразличие к похвалам и критике.\n",
    "3) Шизотипическое (Schizotypal) — Странные убеждения или магическое мышление; поведение или речь, которые кажутся странными, эксцентричными или необычными.\n",
    "4) Антисоциальное (Antisocial) — Пренебрежение к истине; импульсивность и неспособность планировать будущее; нарушение социальных норм.\n",
    "5) Пограничное (Borderline) — Неуместная злость; нестабильные и интенсивные отношения, которые чередуются между идеализацией и обесцениванием.\n",
    "6) Истерическое (Histrionic) — Чрезмерная эмоциональность и стремление к вниманию; драматизация, театральное поведение и преувеличенное выражение эмоций.\n",
    "7) Нарциссическое (Narcissistic) — Высокомерные и надменные манеры или установки, преувеличенное чувство собственной значимости и права на особое отношение.\n",
    "8) Избегающее (Avoidant) — Социальное избегание; чувство неполноценности и повышенная чувствительность к критике или отказу.\n",
    "9) Зависимое (Dependent) — Трудности в принятии повседневных решений без чрезмерных советов и уверений; трудности в выражении несогласия из-за страха потери поддержки или одобрения.\n",
    "10) Обсессивно-компульсивное личностное расстройство (OCPD) — Чрезмерная озабоченность порядком, перфекционизмом, контролем и деталями; стремление к соблюдению правил, часто в ущерб гибкости и эффективности.\n",
    "\n",
    "\n",
    "Пользователь может установить свои коэффициенты корреляции"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "8388ff45",
   "metadata": {},
   "outputs": [],
   "source": [
    "url = 'https://download.sberdisk.ru/download/file/493644095?token=EX7hFxNJhMoLumI&filename=df_mbti_correlation.csv'\n",
    "df_correlation_coefficients_mbti = pd.read_csv(url)\n",
    "\n",
    "df_correlation_coefficients_mbti.index.name = 'ID'\n",
    "df_correlation_coefficients_mbti.index += 1\n",
    "df_correlation_coefficients_mbti.index = df_correlation_coefficients_mbti.index.map(str)\n",
    "\n",
    "url = 'https://download.sberdisk.ru/download/file/493644096?token=T309xfzRosPj3v9&filename=df_disorder_correlation.csv'\n",
    "df_correlation_coefficients_disorders = pd.read_csv(url)\n",
    "\n",
    "df_correlation_coefficients_disorders.index.name = 'ID'\n",
    "df_correlation_coefficients_disorders.index += 1\n",
    "df_correlation_coefficients_disorders.index = df_correlation_coefficients_disorders.index.map(str)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "550b8bb1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Path</th>\n",
       "      <th>OPE</th>\n",
       "      <th>CON</th>\n",
       "      <th>EXT</th>\n",
       "      <th>AGR</th>\n",
       "      <th>NNEU</th>\n",
       "      <th>MBTI</th>\n",
       "      <th>Disorder 1</th>\n",
       "      <th>Disorder 2</th>\n",
       "      <th>Disorder 3</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Person ID</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>speaker_01_center_83.mov</td>\n",
       "      <td>0.596</td>\n",
       "      <td>0.543</td>\n",
       "      <td>0.441</td>\n",
       "      <td>0.590</td>\n",
       "      <td>0.515</td>\n",
       "      <td>ESFJ</td>\n",
       "      <td>Narcissistic (0.037)</td>\n",
       "      <td>Paranoid (0.036)</td>\n",
       "      <td>Dependent (0.03)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>speaker_06_center_83.mov</td>\n",
       "      <td>0.661</td>\n",
       "      <td>0.674</td>\n",
       "      <td>0.603</td>\n",
       "      <td>0.645</td>\n",
       "      <td>0.643</td>\n",
       "      <td>ENFP</td>\n",
       "      <td>Paranoid (0.061)</td>\n",
       "      <td>Histrionic (0.06)</td>\n",
       "      <td>Narcissistic (0.057)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>speaker_07_center_83.mov</td>\n",
       "      <td>0.440</td>\n",
       "      <td>0.465</td>\n",
       "      <td>0.285</td>\n",
       "      <td>0.423</td>\n",
       "      <td>0.396</td>\n",
       "      <td>ISTJ</td>\n",
       "      <td>Schizoid (0.033)</td>\n",
       "      <td>Avoidant (0.019)</td>\n",
       "      <td>Dependent (0.009)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>speaker_10_center_83.mov</td>\n",
       "      <td>0.477</td>\n",
       "      <td>0.503</td>\n",
       "      <td>0.374</td>\n",
       "      <td>0.441</td>\n",
       "      <td>0.425</td>\n",
       "      <td>INTJ</td>\n",
       "      <td>Schizoid (0.03)</td>\n",
       "      <td>OCPD (0.021)</td>\n",
       "      <td>Avoidant (0.017)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>speaker_11_center_83.mov</td>\n",
       "      <td>0.403</td>\n",
       "      <td>0.344</td>\n",
       "      <td>0.317</td>\n",
       "      <td>0.422</td>\n",
       "      <td>0.384</td>\n",
       "      <td>ISTJ</td>\n",
       "      <td>Schizoid (0.032)</td>\n",
       "      <td>Avoidant (0.018)</td>\n",
       "      <td>Dependent (0.01)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>speaker_15_center_83.mov</td>\n",
       "      <td>0.582</td>\n",
       "      <td>0.562</td>\n",
       "      <td>0.505</td>\n",
       "      <td>0.602</td>\n",
       "      <td>0.522</td>\n",
       "      <td>ENFP</td>\n",
       "      <td>Paranoid (0.06)</td>\n",
       "      <td>Narcissistic (0.057)</td>\n",
       "      <td>Histrionic (0.054)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>speaker_19_center_83.mov</td>\n",
       "      <td>0.510</td>\n",
       "      <td>0.448</td>\n",
       "      <td>0.426</td>\n",
       "      <td>0.452</td>\n",
       "      <td>0.448</td>\n",
       "      <td>INFP</td>\n",
       "      <td>Schizoid (0.036)</td>\n",
       "      <td>Avoidant (0.036)</td>\n",
       "      <td>Narcissistic (0.03)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>speaker_23_center_83.mov</td>\n",
       "      <td>0.501</td>\n",
       "      <td>0.541</td>\n",
       "      <td>0.309</td>\n",
       "      <td>0.441</td>\n",
       "      <td>0.452</td>\n",
       "      <td>INTJ</td>\n",
       "      <td>OCPD (0.012)</td>\n",
       "      <td>Schizoid (0.01)</td>\n",
       "      <td>Avoidant (0.006)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>speaker_24_center_83.mov</td>\n",
       "      <td>0.428</td>\n",
       "      <td>0.511</td>\n",
       "      <td>0.301</td>\n",
       "      <td>0.434</td>\n",
       "      <td>0.442</td>\n",
       "      <td>INTJ</td>\n",
       "      <td>Schizoid (0.026)</td>\n",
       "      <td>OCPD (0.02)</td>\n",
       "      <td>Avoidant (0.015)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>speaker_27_center_83.mov</td>\n",
       "      <td>0.566</td>\n",
       "      <td>0.659</td>\n",
       "      <td>0.434</td>\n",
       "      <td>0.591</td>\n",
       "      <td>0.579</td>\n",
       "      <td>ENFJ</td>\n",
       "      <td>Narcissistic (0.026)</td>\n",
       "      <td>Paranoid (0.026)</td>\n",
       "      <td>Dependent (0.021)</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                               Path    OPE    CON    EXT    AGR   NNEU  MBTI  \\\n",
       "Person ID                                                                      \n",
       "1          speaker_01_center_83.mov  0.596  0.543  0.441  0.590  0.515  ESFJ   \n",
       "2          speaker_06_center_83.mov  0.661  0.674  0.603  0.645  0.643  ENFP   \n",
       "3          speaker_07_center_83.mov  0.440  0.465  0.285  0.423  0.396  ISTJ   \n",
       "4          speaker_10_center_83.mov  0.477  0.503  0.374  0.441  0.425  INTJ   \n",
       "5          speaker_11_center_83.mov  0.403  0.344  0.317  0.422  0.384  ISTJ   \n",
       "6          speaker_15_center_83.mov  0.582  0.562  0.505  0.602  0.522  ENFP   \n",
       "7          speaker_19_center_83.mov  0.510  0.448  0.426  0.452  0.448  INFP   \n",
       "8          speaker_23_center_83.mov  0.501  0.541  0.309  0.441  0.452  INTJ   \n",
       "9          speaker_24_center_83.mov  0.428  0.511  0.301  0.434  0.442  INTJ   \n",
       "10         speaker_27_center_83.mov  0.566  0.659  0.434  0.591  0.579  ENFJ   \n",
       "\n",
       "                     Disorder 1            Disorder 2            Disorder 3  \n",
       "Person ID                                                                    \n",
       "1          Narcissistic (0.037)      Paranoid (0.036)      Dependent (0.03)  \n",
       "2              Paranoid (0.061)     Histrionic (0.06)  Narcissistic (0.057)  \n",
       "3              Schizoid (0.033)      Avoidant (0.019)     Dependent (0.009)  \n",
       "4               Schizoid (0.03)          OCPD (0.021)      Avoidant (0.017)  \n",
       "5              Schizoid (0.032)      Avoidant (0.018)      Dependent (0.01)  \n",
       "6               Paranoid (0.06)  Narcissistic (0.057)    Histrionic (0.054)  \n",
       "7              Schizoid (0.036)      Avoidant (0.036)   Narcissistic (0.03)  \n",
       "8                  OCPD (0.012)       Schizoid (0.01)      Avoidant (0.006)  \n",
       "9              Schizoid (0.026)           OCPD (0.02)      Avoidant (0.015)  \n",
       "10         Narcissistic (0.026)      Paranoid (0.026)     Dependent (0.021)  "
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "_b5._colleague_personality_desorders(\n",
    "    correlation_coefficients_mbti = df_correlation_coefficients_mbti,\n",
    "    correlation_coefficients_disorders = df_correlation_coefficients_disorders,\n",
    "    personality_desorder_number = 3,\n",
    "    threshold = 0.5,\n",
    "    out = True\n",
    ")\n",
    "\n",
    "_b5._save_logs(df = _b5.df_files_MBTI_disorders_, name = 'MBTI_colleague_personality_type_match_fi_en', out = True)\n",
    "\n",
    "# Optional\n",
    "df = _b5.df_files_MBTI_disorders_.rename(columns = {'Openness':'OPE', 'Conscientiousness':'CON', 'Extraversion': 'EXT', 'Agreeableness': 'AGR', 'Non-Neuroticism': 'NNEU'})\n",
    "columns_to_round = df.columns[1:6]\n",
    "df[columns_to_round] = df[columns_to_round].apply(lambda x: [round(i, 3) for i in x])\n",
    "df"
   ]
  }
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