{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "aa1a1b54-d8db-4524-b60c-270df12c8db4",
   "metadata": {},
   "source": [
    "## Решение практической задачи 1\n",
    "\n",
    "### Задача: ранжирование потенциальных кандидатов по профессиональным обязанностям\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 методы _candidate_ranking и _priority_skill_calculation из библиотеки OCEAN-AI для решения представленной практической задачи. Примеры результатов работы и реализации представлены ниже.\n",
    "\n",
    "> Таким образом, библиотека OCEAN-AI предоставляет инструменты для анализа персональных качеств личности кандидатов и их соответствия требованиям должности, что может значительно улучшить процесс подбора персонала и помочь в принятии более объективных и систематизированных решений при ранжировании кандидатов.\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_1_en.svg\" width=\"100%\"/>\n",
    "</center>\n",
    "\n",
    "<hr>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "f5d4b7ce-ce69-43b9-9145-c785e9e62091",
   "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": "c6b59162-9490-446c-85ca-7ce2a9f8ac48",
   "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 17:13:14</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>"
      ]
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     "output_type": "display_data"
    },
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      "text/markdown": [
       "<span style=\"color:#666\">**[</span><span style=\"color:#1776D2\">2024-10-10 17:13:15</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>"
      ],
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       "<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 17:13:15</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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       "</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>0.0735</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",
       "  </tbody>\n",
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      ],
      "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  "
      ]
     },
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    },
    {
     "data": {
      "text/markdown": [
       "<span style=\"color:#666\">**[</span><span style=\"color:#1776D2\">2024-10-10 17:13:15</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: 34.472 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_bert_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": "bd399157-db76-4f5d-9324-c53fde65ccbe",
   "metadata": {},
   "source": [
    "<hr>\n",
    "\n",
    "Для выполнения ранжирования кандидатов необходимо знать весовые коэффициенты, определяющие приоритетность персональных качеств личности в зависимости от профессии.\n",
    "\n",
    "Предлагаются весовые коэффициенты для 5 профессий, вычисленные на основе научных статей:\n",
    "\n",
    "1) Sajjad H. et al. Personality and Career Choices // African Journal of Business Management. - 2012. – Vol. 6 (6) – pp. 2255-2260.\n",
    "2) Alkhelil A. H. The Relationship between Personality Traits and Career Choice: A Case Study of Secondary School Students // International Journal of Academic Research in Progressive Education and Development. – 2016. – Vol. 5(2). – pp. 2226-6348.\n",
    "3) De Jong N. et al. Personality Traits and Career Role Enactment: Career Role Preferences as a Mediator // Frontiers in Psychology. – 2019. – Vol. 10. – pp. 1720.\n",
    "\n",
    "Пользователь может установить свои весовые коэффициенты; сумма весов должна быть равна 100."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "e8d85c2a-1825-4f13-8ac7-a9033aa9a69a",
   "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>Profession</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>Managers/executives</td>\n",
       "      <td>15</td>\n",
       "      <td>35</td>\n",
       "      <td>15</td>\n",
       "      <td>30</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Entrepreneurship</td>\n",
       "      <td>30</td>\n",
       "      <td>30</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Social/Non profit making professions</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>35</td>\n",
       "      <td>35</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Public sector professions</td>\n",
       "      <td>15</td>\n",
       "      <td>50</td>\n",
       "      <td>15</td>\n",
       "      <td>15</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Scientists/researchers, and engineers</td>\n",
       "      <td>50</td>\n",
       "      <td>15</td>\n",
       "      <td>5</td>\n",
       "      <td>15</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                               Profession  Openness  Conscientiousness  \\\n",
       "ID                                                                       \n",
       "1                     Managers/executives        15                 35   \n",
       "2                        Entrepreneurship        30                 30   \n",
       "3    Social/Non profit making professions         5                  5   \n",
       "4               Public sector professions        15                 50   \n",
       "5   Scientists/researchers, and engineers        50                 15   \n",
       "\n",
       "    Extraversion  Agreeableness  Non-Neuroticism  \n",
       "ID                                                \n",
       "1             15             30                5  \n",
       "2              5              5               30  \n",
       "3             35             35               20  \n",
       "4             15             15                5  \n",
       "5              5             15               15  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Загрузка датафрейма с весовыми коэффициентами\n",
    "url = 'https://download.sberdisk.ru/download/file/478675798?token=fF5fNZVpthQlEV0&filename=traits_priority_for_professions.csv'\n",
    "traits_priority_for_professions = pd.read_csv(url)\n",
    "\n",
    "traits_priority_for_professions.index.name = 'ID'\n",
    "traits_priority_for_professions.index += 1\n",
    "traits_priority_for_professions.index = traits_priority_for_professions.index.map(str)\n",
    "\n",
    "traits_priority_for_professions"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8fd37b5f-eb10-417c-99b3-67c56001b2fc",
   "metadata": {},
   "source": [
    "#### Ранжирование кандидатов на должность инженера-проектировщика"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "c35d30c6-1042-4bd3-abbc-48081e44fd06",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
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       "\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
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       "</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>Candidate score</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>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>64.644</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>62.472</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>62.080</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>61.812</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>58.876</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>58.412</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>53.134</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>47.712</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>46.438</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>45.310</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                          Path    OPE    CON    EXT    AGR   NNEU  \\\n",
       "Person ID                                                           \n",
       "5          be0DQawtVkE.002.mp4  0.681  0.566  0.554  0.647  0.642   \n",
       "10         _plk5k7PBEg.003.mp4  0.648  0.610  0.525  0.614  0.606   \n",
       "1          2d6btbaNdfo.000.mp4  0.619  0.661  0.478  0.654  0.601   \n",
       "6          cLaZxEf1nE4.004.mp4  0.663  0.551  0.558  0.585  0.587   \n",
       "4          4vdJGgZpj4k.003.mp4  0.585  0.616  0.494  0.606  0.587   \n",
       "8          JZNMxa3OKHY.000.mp4  0.606  0.524  0.531  0.594  0.580   \n",
       "7          g24JGYuT74A.004.mp4  0.590  0.399  0.410  0.532  0.507   \n",
       "9          nvlqJbHk_Lc.003.mp4  0.511  0.465  0.391  0.444  0.439   \n",
       "3          300gK3CnzW0.003.mp4  0.468  0.449  0.372  0.510  0.454   \n",
       "2          300gK3CnzW0.001.mp4  0.462  0.413  0.416  0.498  0.431   \n",
       "\n",
       "           Candidate score  \n",
       "Person ID                   \n",
       "5                   64.644  \n",
       "10                  62.472  \n",
       "1                   62.080  \n",
       "6                   61.812  \n",
       "4                   58.876  \n",
       "8                   58.412  \n",
       "7                   53.134  \n",
       "9                   47.712  \n",
       "3                   46.438  \n",
       "2                   45.310  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "weights = traits_priority_for_professions.iloc[4].values[1:]\n",
    "weights = list(map(int, weights))\n",
    "\n",
    "_b5._candidate_ranking(\n",
    "    weigths_openness = weights[0], \n",
    "    weigths_conscientiousness = weights[1],\n",
    "    weigths_extraversion = weights[2],\n",
    "    weigths_agreeableness = weights[3], \n",
    "    weigths_non_neuroticism = weights[4],\n",
    "    out = False\n",
    ")\n",
    "\n",
    "_b5._save_logs(df = _b5.df_files_ranking_, name = 'engineer_candidate_ranking_fi_en', out = True)\n",
    "\n",
    "# Опционно\n",
    "df = _b5.df_files_ranking_.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": "db44803a-fb15-4540-883d-1231b5115a0b",
   "metadata": {},
   "source": [
    "#### Ранжирование кандидатов на должность менеджера"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "d3c9a238-a39a-4bdd-9bb1-92ed194badb0",
   "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",
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       "    <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>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>62.212</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>60.943</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>60.412</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>58.876</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>58.099</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>56.112</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>47.463</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>45.855</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>45.297</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>44.737</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                          Path    OPE    CON    EXT    AGR   NNEU  \\\n",
       "Person ID                                                           \n",
       "1          2d6btbaNdfo.000.mp4  0.619  0.661  0.478  0.654  0.601   \n",
       "5          be0DQawtVkE.002.mp4  0.681  0.566  0.554  0.647  0.642   \n",
       "10         _plk5k7PBEg.003.mp4  0.648  0.610  0.525  0.614  0.606   \n",
       "4          4vdJGgZpj4k.003.mp4  0.585  0.616  0.494  0.606  0.587   \n",
       "6          cLaZxEf1nE4.004.mp4  0.663  0.551  0.558  0.585  0.587   \n",
       "8          JZNMxa3OKHY.000.mp4  0.606  0.524  0.531  0.594  0.580   \n",
       "7          g24JGYuT74A.004.mp4  0.590  0.399  0.410  0.532  0.507   \n",
       "3          300gK3CnzW0.003.mp4  0.468  0.449  0.372  0.510  0.454   \n",
       "9          nvlqJbHk_Lc.003.mp4  0.511  0.465  0.391  0.444  0.439   \n",
       "2          300gK3CnzW0.001.mp4  0.462  0.413  0.416  0.498  0.431   \n",
       "\n",
       "           Candidate score  \n",
       "Person ID                   \n",
       "1                   62.212  \n",
       "5                   60.943  \n",
       "10                  60.412  \n",
       "4                   58.876  \n",
       "6                   58.099  \n",
       "8                   56.112  \n",
       "7                   47.463  \n",
       "3                   45.855  \n",
       "9                   45.297  \n",
       "2                   44.737  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "weights = traits_priority_for_professions.iloc[0].values[1:]\n",
    "weights = list(map(int, weights))\n",
    "\n",
    "_b5._candidate_ranking(\n",
    "    weigths_openness = weights[0], \n",
    "    weigths_conscientiousness = weights[1],\n",
    "    weigths_extraversion = weights[2],\n",
    "    weigths_agreeableness = weights[3], \n",
    "    weigths_non_neuroticism = weights[4],\n",
    "    out = False\n",
    ")\n",
    "\n",
    "_b5._save_logs(df = _b5.df_files_ranking_, name = 'executive_candidate_ranking_fi_en', out = True)\n",
    "\n",
    "# Опционно\n",
    "df = _b5.df_files_ranking_.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": "8509b13f-f215-4fd7-aa0c-a3e117e967fb",
   "metadata": {},
   "source": [
    "<hr>\n",
    "\n",
    "Для ранжирования кандидатов по профессиональным навыкам необходимо задать по два коэффициента корреляции для каждого персонального качества личности человека и навыка, а также порога полярности качеств. Эти коэффициенты должны показывать, как измениться оценка качества человека если она больше или меньше заданного порога полярности качеств. \n",
    "\n",
    "В качестве примера предлагается использование коэффициентов корреляции между двумя людьми в четырьмя профессиональными навыками, представленных в статье:\n",
    "\n",
    "1) Wehner C., de Grip A., Pfeifer H. Do recruiters select workers with different personality traits for different tasks? A discrete choice experiment // Labour Economics. - 2022. - vol. 78. - pp. 102186.\n",
    "\n",
    "Представлены 4 профессиональных навыка: \n",
    "\n",
    "1) Analytical (Аналитические навыки). Умение эффективно решать новые задачи, требующие глубокого анализа. \n",
    "2) Interactive (Навыки межличностного общения). Умение убеждать и достигать компромиссов с заказчиками и коллегами.\n",
    "4) Routine (Способность выполнять рутинную работу). Умение эффективно управлять рутинными задачами, соблюдая точность и внимание к деталям.\n",
    "5) Non-Routine (Способность выполнять нестандартную работу). Умение реагировать и решать проблемы, не имеющие установленного порядка, проявляя адаптивность и креативные навыки в решении задач.\n",
    "\n",
    "Пользователь может установить свои коэффициенты корреляции и ранжировать кандидатов по другим профессиональным навыкам."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "baed7e14-4b47-48bf-9109-015bb5b7d89c",
   "metadata": {},
   "source": [
    "#### Ранжирование кандидатов по профессиональным навыкам"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "8a9fd13a-f2f9-4ad3-9ed7-496859876002",
   "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>Score_level</th>\n",
       "      <th>Analytical</th>\n",
       "      <th>Interactive</th>\n",
       "      <th>Routine</th>\n",
       "      <th>Non-Routine</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>Openness</td>\n",
       "      <td>high</td>\n",
       "      <td>0.082</td>\n",
       "      <td>0.348</td>\n",
       "      <td>0.571</td>\n",
       "      <td>0.510</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Openness</td>\n",
       "      <td>low</td>\n",
       "      <td>0.196</td>\n",
       "      <td>0.152</td>\n",
       "      <td>0.148</td>\n",
       "      <td>0.218</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Conscientiousness</td>\n",
       "      <td>high</td>\n",
       "      <td>0.994</td>\n",
       "      <td>1.333</td>\n",
       "      <td>1.507</td>\n",
       "      <td>1.258</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Conscientiousness</td>\n",
       "      <td>low</td>\n",
       "      <td>0.241</td>\n",
       "      <td>0.188</td>\n",
       "      <td>0.191</td>\n",
       "      <td>0.267</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Extraversion</td>\n",
       "      <td>high</td>\n",
       "      <td>0.169</td>\n",
       "      <td>-0.060</td>\n",
       "      <td>0.258</td>\n",
       "      <td>0.017</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Extraversion</td>\n",
       "      <td>low</td>\n",
       "      <td>0.181</td>\n",
       "      <td>0.135</td>\n",
       "      <td>0.130</td>\n",
       "      <td>0.194</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Agreeableness</td>\n",
       "      <td>high</td>\n",
       "      <td>1.239</td>\n",
       "      <td>0.964</td>\n",
       "      <td>1.400</td>\n",
       "      <td>1.191</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Agreeableness</td>\n",
       "      <td>low</td>\n",
       "      <td>0.226</td>\n",
       "      <td>0.180</td>\n",
       "      <td>0.189</td>\n",
       "      <td>0.259</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Non-Neuroticism</td>\n",
       "      <td>high</td>\n",
       "      <td>0.636</td>\n",
       "      <td>0.777</td>\n",
       "      <td>0.876</td>\n",
       "      <td>0.729</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Non-Neuroticism</td>\n",
       "      <td>low</td>\n",
       "      <td>0.207</td>\n",
       "      <td>0.159</td>\n",
       "      <td>0.166</td>\n",
       "      <td>0.238</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                Trait Score_level  Analytical  Interactive  Routine  \\\n",
       "ID                                                                    \n",
       "1            Openness        high       0.082        0.348    0.571   \n",
       "2            Openness         low       0.196        0.152    0.148   \n",
       "3   Conscientiousness        high       0.994        1.333    1.507   \n",
       "4   Conscientiousness         low       0.241        0.188    0.191   \n",
       "5        Extraversion        high       0.169       -0.060    0.258   \n",
       "6        Extraversion         low       0.181        0.135    0.130   \n",
       "7       Agreeableness        high       1.239        0.964    1.400   \n",
       "8       Agreeableness         low       0.226        0.180    0.189   \n",
       "9     Non-Neuroticism        high       0.636        0.777    0.876   \n",
       "10    Non-Neuroticism         low       0.207        0.159    0.166   \n",
       "\n",
       "    Non-Routine  \n",
       "ID               \n",
       "1         0.510  \n",
       "2         0.218  \n",
       "3         1.258  \n",
       "4         0.267  \n",
       "5         0.017  \n",
       "6         0.194  \n",
       "7         1.191  \n",
       "8         0.259  \n",
       "9         0.729  \n",
       "10        0.238  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Загрузка датафрейма с коэффициентами корреляции\n",
    "url = 'https://download.sberdisk.ru/download/file/478678231?token=0qiZwliLtHWWYMv&filename=professional_skills.csv'\n",
    "df_professional_skills = pd.read_csv(url)\n",
    "\n",
    "df_professional_skills.index.name = 'ID'\n",
    "df_professional_skills.index += 1\n",
    "df_professional_skills.index = df_professional_skills.index.map(str)\n",
    "\n",
    "df_professional_skills"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "9529e4e5-ca6c-4539-a311-beed1d07364a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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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>Analytical</th>\n",
       "      <th>Interactive</th>\n",
       "      <th>Routine</th>\n",
       "      <th>Non-Routine</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>0.397</td>\n",
       "      <td>0.452</td>\n",
       "      <td>0.571</td>\n",
       "      <td>0.491</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>0.384</td>\n",
       "      <td>0.416</td>\n",
       "      <td>0.571</td>\n",
       "      <td>0.461</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>0.379</td>\n",
       "      <td>0.414</td>\n",
       "      <td>0.563</td>\n",
       "      <td>0.456</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>0.375</td>\n",
       "      <td>0.426</td>\n",
       "      <td>0.538</td>\n",
       "      <td>0.464</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>0.359</td>\n",
       "      <td>0.390</td>\n",
       "      <td>0.537</td>\n",
       "      <td>0.433</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>0.353</td>\n",
       "      <td>0.380</td>\n",
       "      <td>0.522</td>\n",
       "      <td>0.421</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>0.240</td>\n",
       "      <td>0.249</td>\n",
       "      <td>0.331</td>\n",
       "      <td>0.298</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>0.198</td>\n",
       "      <td>0.154</td>\n",
       "      <td>0.198</td>\n",
       "      <td>0.202</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>0.093</td>\n",
       "      <td>0.072</td>\n",
       "      <td>0.073</td>\n",
       "      <td>0.105</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>0.083</td>\n",
       "      <td>0.094</td>\n",
       "      <td>0.118</td>\n",
       "      <td>0.136</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                          Path    OPE    CON    EXT    AGR   NNEU  Analytical  \\\n",
       "Person ID                                                                       \n",
       "1          2d6btbaNdfo.000.mp4  0.619  0.661  0.478  0.654  0.601       0.397   \n",
       "5          be0DQawtVkE.002.mp4  0.681  0.566  0.554  0.647  0.642       0.384   \n",
       "10         _plk5k7PBEg.003.mp4  0.648  0.610  0.525  0.614  0.606       0.379   \n",
       "4          4vdJGgZpj4k.003.mp4  0.585  0.616  0.494  0.606  0.587       0.375   \n",
       "6          cLaZxEf1nE4.004.mp4  0.663  0.551  0.558  0.585  0.587       0.359   \n",
       "8          JZNMxa3OKHY.000.mp4  0.606  0.524  0.531  0.594  0.580       0.353   \n",
       "7          g24JGYuT74A.004.mp4  0.590  0.399  0.410  0.532  0.507       0.240   \n",
       "3          300gK3CnzW0.003.mp4  0.468  0.449  0.372  0.510  0.454       0.198   \n",
       "2          300gK3CnzW0.001.mp4  0.462  0.413  0.416  0.498  0.431       0.093   \n",
       "9          nvlqJbHk_Lc.003.mp4  0.511  0.465  0.391  0.444  0.439       0.083   \n",
       "\n",
       "           Interactive  Routine  Non-Routine  \n",
       "Person ID                                     \n",
       "1                0.452    0.571        0.491  \n",
       "5                0.416    0.571        0.461  \n",
       "10               0.414    0.563        0.456  \n",
       "4                0.426    0.538        0.464  \n",
       "6                0.390    0.537        0.433  \n",
       "8                0.380    0.522        0.421  \n",
       "7                0.249    0.331        0.298  \n",
       "3                0.154    0.198        0.202  \n",
       "2                0.072    0.073        0.105  \n",
       "9                0.094    0.118        0.136  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "_b5._priority_skill_calculation(\n",
    "    correlation_coefficients = df_professional_skills,\n",
    "    threshold = 0.5,\n",
    "    out = True\n",
    ")\n",
    "\n",
    "_b5._save_logs(df = _b5.df_files_priority_skill_, name = 'skill_candidate_ranking_fi_en', out = True)\n",
    "\n",
    "# Опционно\n",
    "df = _b5.df_files_priority_skill_.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": "a4283405",
   "metadata": {},
   "source": [
    "<hr>\n",
    "\n",
    "Для ранжирования кандидатов по одному из шестнадцати типов личности MBTI необходимо задать матрицу корреляции между персональными качествами личности человека и четырьмя диспозициями MBTI, установить порог полярности качеств и указать целевой тип личности MBTI.\n",
    "\n",
    "В качестве примера предлагается использование коэффициентов корреляции, представленных в статье [1]. Описание типов личности MBTI и соотвествующие им успешные профессии представлены в статье [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) Tieger P.D., Barron B., Tieger K. Do what you are: Discover the perfect career for you through the secrets of personality type // Hachette UK. - 2024.\n",
    "\n",
    "##### Типы личности MBTI основаны на четырех измерениях личности:\n",
    "\n",
    "<div style=\"text-align: center;\">\n",
    "  <table style=\"width:100%; margin: 0 auto; font-size: 0.6em;\">\n",
    "    <tr>\n",
    "      <th>Описание измерения</th>\n",
    "      <th>Измерение</th>\n",
    "    </tr>\n",
    "    <tr>\n",
    "      <td>Как мы взаимодействуем с миром и куда направляем свою энергию</td>\n",
    "      <td>(E) Экстраверсия - Интроверсия (I)</td>\n",
    "    </tr>\n",
    "    <tr>\n",
    "      <td>Вид информации, которую мы естественным образом замечаем</td>\n",
    "      <td>(S) Сенсорика - Интуиция (N)</td>\n",
    "    </tr>\n",
    "    <tr>\n",
    "      <td>Как мы принимаем решения</td>\n",
    "      <td>(T) Логика - Чувства (F)</td>\n",
    "    </tr>\n",
    "    <tr>\n",
    "      <td>Предпочитаем ли мы жить более структурированно (принимая решения) или более спонтанно (принимая информацию)</td>\n",
    "      <td>(J) Оценка - Восприятие (P)</td>\n",
    "    </tr>\n",
    "  </table>\n",
    "</div>\n",
    "\n",
    "##### Типы личности MBTI и соотвествующие им успешные профессии:\n",
    "\n",
    "<div style=\"text-align: center;\">\n",
    "  <table style=\"width: 100%; margin: 0 auto; font-size: 0.6em;\">\n",
    "    <tr>\n",
    "      <th>Тип личности</th>\n",
    "      <th>Описание</th>\n",
    "      <th>Успешные профессии</th>\n",
    "    </tr>\n",
    "    <tr>\n",
    "      <td>ISTJ</td>\n",
    "      <td>Этот человек отличается ответственностью, строгостью и педантичностью. Он опирается на объективные факты и склонен к аналитическому мышлению. Приступает к задаче только тогда, когда уверен в своих возможностях и успехе</td>\n",
    "      <td>Инспектор: бухгалтер, аудитор, бюджетный аналитик, финансовый менеджер, разработчик, системный аналитик, библиотекарь и т. д.</td>\n",
    "    </tr>\n",
    "    <tr>\n",
    "      <td>ISFJ</td>\n",
    "      <td>Этот человек склонен к самоанализу и анализу окружающих, легко распознает фальшь и предпочитает сохранять психологическую дистанцию. Он исполнителен, внимателен и готов помогать другим. Его силы и энергия исходят из внутренних ресурсов, и он всегда полагается на собственный опыт</td>\n",
    "      <td>Защитник: медсестра, врач, ветеринар или ветеринарный ассистент, социальный работник, сельскохозяйственный или пищевой ученый, секретарь, водитель и т. д.</td>\n",
    "    </tr>\n",
    "    <tr>\n",
    "      <td>INFJ</td>\n",
    "      <td>О таких людях говорят: «ему можно доверять». Он отличается высокой чувствительностью, уделяет большое внимание межличностным отношениям, умеет давать ценные советы и помогает раскрывать потенциал других. Развитая интуиция не только генерирует множество идей, но и способствует самоорганизации</td>\n",
    "      <td>Советник: психолог, специалист по управлению персоналом, офис-менеджер, специалист по обучению, графический дизайнер и т. д.</td>\n",
    "    </tr>\n",
    "    <tr>\n",
    "      <td>INTJ</td>\n",
    "      <td>Этот человек умеет выделять главное, говорит четко и по существу, придерживается практического подхода. Он стремится постоянно улучшать свою работу и всегда ищет способы сделать задачу еще лучше. Пустые разговоры ему не по душе, поэтому он избегает больших шумных компаний и с трудом заводит новые знакомства</td>\n",
    "      <td>Мастермайнд: аниматор, архитектор, копирайтер, фотограф, тележурналист, видеомонтажер, специалист по бизнес-развитию, исполнительный директор, профессор и т. д.</td>\n",
    "    </tr>\n",
    "    <tr>\n",
    "      <td>ISTP</td>\n",
    "      <td>Этот человек воспринимает мир через ощущения. По природе эмпат, но чаще сосредоточен на себе. Его умение объективно принимать решения и анализировать ситуацию указывает на технический склад ума. Он всегда соблюдает дедлайны, хотя иногда может поступить неожиданно</td>\n",
    "      <td>Создатель: инженер, техник, строитель, инспектор, судебный эксперт, программист, разработчик ПО и т. д.</td>\n",
    "    </tr>\n",
    "    <tr>\n",
    "      <td>ISFP</td>\n",
    "      <td>Этот человек умеет находить радость в однообразии и рутинных делах. Прекрасно ладит с людьми, избегая конфликтов. Ему важно чувствовать свою значимость и оказывать помощь. Такой человек не стремится руководить или менять других, уважает их личные границы и ожидает того же в ответ. По натуре он приземленный практик, на которого всегда можно положиться</td>\n",
    "      <td>Композитор: помощник по маркетингу, танцор, шеф-повар, офис-администратор, художник, дизайнер интерьеров, секретарь, медсестра и т. д.</td>\n",
    "    </tr>\n",
    "    <tr>\n",
    "      <td>INFP</td>\n",
    "      <td>Этот человек - чувствительный лирик, прекрасно разбирающийся в людях и легко вызывающий у них симпатию. Он обладает отличным чувством юмора и уделяет большое внимание своему внешнему виду. Стремится к самопознанию, гармонии с собой и старается быть полезным окружающим</td>\n",
    "      <td>Целитель: писатель, дизайнер мультимедиа, менеджер по работе с клиентами, учитель для детей с особыми потребностями, тренер, редактор, модельер и т. д.</td>\n",
    "    </tr>\n",
    "    <tr>\n",
    "      <td>INTP</td>\n",
    "      <td>Этот человек - эрудит с философским складом ума. Он тщательно анализирует свои решения, стремясь к объективности и беспристрастности. Бурные проявления эмоций ему не свойственны. Однако большое количество данных и их изменчивость могут вызывать у него внутреннее напряжение</td>\n",
    "      <td>Архитектор: технический писатель, веб-разработчик, аналитик информационной безопасности, исследователь, ученый, юрист и т. д.</td>\n",
    "    </tr>\n",
    "    <tr>\n",
    "      <td>ESTP</td>\n",
    "      <td>Этот человек всегда добивается успеха, невзирая на препятствия, которые лишь усиливают его целеустремленность. Он стремится к лидерским позициям и плохо переносит роль подчиненного. Обычно разрабатывает четкий план действий и неуклонно ему следует</td>\n",
    "      <td>Промоутер: специалист по работе с клиентами, актер, личный тренер, бренд-амбассадор, менеджер, предприниматель, креативный директор, полицейский, маркетолог, производитель и т. д.</td>\n",
    "    </tr>\n",
    "    <tr>\n",
    "      <td>ESFP</td>\n",
    "      <td>Этот человек легко выявляет слабые стороны людей, что позволяет ему эффективно манипулировать и управлять. В общении он чаще всего руководствуется собственными интересами и предпочитает жить в настоящем. Часто не завершает начатое, стремясь к быстрым результатам. Однако при этом стремится поддерживать гармоничные отношения с окружающими</td>\n",
    "      <td>Исполнитель: бортпроводник, артист, учитель, менеджер по связям с общественностью, торговый представитель, организатор мероприятий и т. д.</td>\n",
    "    </tr>\n",
    "    <tr>\n",
    "      <td>ENFP</td>\n",
    "      <td>Этот человек - творческая личность и фантазер, обладающий качествами, которые помогают ему успешно взаимодействовать с другими, быть открытым и общительным. Он активно участвует в различных мероприятиях, легко решает возникающие вопросы и демонстрирует гибкость</td>\n",
    "      <td>Чемпион: медицинский работник, продюсер, продавец-консультант, специалист по обслуживанию клиентов, сценарист, ведущий на ТВ/радио и т. д.</td>\n",
    "    </tr>\n",
    "    <tr>\n",
    "      <td>ENTP</td>\n",
    "      <td>Этот человек - изобретательный, инициативный и гибкий. Он генератор идей и первопроходец, который не выносит рутины. Постоянное движение и интуитивное принятие решений всегда сопровождают его в работе</td>\n",
    "      <td>Новатор: инженер, маркетолог, менеджер по социальным сетям, аналитик управления, руководитель цифрового маркетинга, бизнес-консультант, разработчик игр, менеджер по продажам и т. д.</td>\n",
    "    </tr>\n",
    "    <tr>\n",
    "      <td>ESTJ</td>\n",
    "      <td>Это трудолюбивый человек, который воспринимает мир таким, какой он есть. Он склонен тщательно планировать и доводить дела до конца. Заботится о своем ближайшем окружении, проявляет добродушие, но иногда может быть вспыльчивым, резким и упрямым</td>\n",
    "      <td>Супервайзер: управляющий директор, менеджер отеля, финансовый сотрудник, судья, агент по недвижимости, генеральный директор, шеф-повар, менеджер по бизнес-развитию, телемаркетолог и т. д.</td>\n",
    "    </tr>\n",
    "    <tr>\n",
    "      <td>ESFJ</td>\n",
    "      <td>Этот человек умеет оказывать влияние на людей, проявляет заботу и готов жертвовать собой ради других. Он легко устанавливает контакт с любым человеком и способен направить ситуацию в нужное ему русло</td>\n",
    "      <td>Поставщик: специалист по технической поддержке, менеджер по работе с клиентами, профессор колледжа, медицинский исследователь, бухгалтер, фотожурналист и т. д.</td>\n",
    "    </tr>\n",
    "    <tr>\n",
    "      <td>ENFJ</td>\n",
    "      <td>Этот человек отличается эмоциональностью и эмпатией. Его мимика выразительна, а речь — красноречива. Благодаря своей самоорганизованности, он успешно воплощает свои фантазии и идеи в жизнь. Он интуитивно понимает, какое решение следует принять в каждой конкретной ситуации</td>\n",
    "      <td>Учитель: менеджер по связям с общественностью, менеджер по продажам, директор по управлению персоналом, арт-директор, консультант и т. д.</td>\n",
    "    </tr>\n",
    "    <tr>\n",
    "      <td>ENTJ</td>\n",
    "      <td>Этот человек легко увлекается, готов рисковать и полагается на интуицию. Без страха внедряет новые технологии и способен глубоко анализировать как себя, так и окружающий мир. Жизнь для него - это борьба, в которой он чувствует себя уверенно. Открыт для новых возможностей, но при этом нуждается в контроле</td>\n",
    "      <td>Командир: руководитель строительства, администратор службы здравоохранения, финансовый бухгалтер, аудитор, юрист, директор школы, химический инженер, менеджер баз данных и т. д.</td>\n",
    "    </tr>\n",
    "  </table>\n",
    "</div>\n",
    "\n",
    "Пользователь может установить свои коэффициенты корреляции и ранжировать кандидатов по другим типам личности."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d0a65bd1",
   "metadata": {},
   "source": [
    "#### Ранжирование кандидатов по одному из шестнадцати типов личности по версии MBTI"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "18dc8c49",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <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",
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      ],
      "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": 8,
     "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": 9,
   "id": "fff740f8",
   "metadata": {},
   "outputs": [
    {
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       "      <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>0.289871</td>\n",
       "      <td>75.0</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>0.277057</td>\n",
       "      <td>75.0</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>0.268961</td>\n",
       "      <td>75.0</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>0.266504</td>\n",
       "      <td>75.0</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>0.166132</td>\n",
       "      <td>50.0</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>0.139850</td>\n",
       "      <td>75.0</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>0.139020</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>0.123449</td>\n",
       "      <td>75.0</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>0.045258</td>\n",
       "      <td>50.0</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>-0.185699</td>\n",
       "      <td>0.017946</td>\n",
       "      <td>0.083205</td>\n",
       "      <td>0.030144</td>\n",
       "      <td>ISTJ</td>\n",
       "      <td>0.007536</td>\n",
       "      <td>25.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                          Path    OPE    CON    EXT    AGR   NNEU        EI  \\\n",
       "Person ID                                                                     \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",
       "10         _plk5k7PBEg.003.mp4  0.648  0.610  0.525  0.614  0.606  0.248447   \n",
       "8          JZNMxa3OKHY.000.mp4  0.606  0.524  0.531  0.594  0.580  0.239967   \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",
       "7          g24JGYuT74A.004.mp4  0.590  0.399  0.410  0.532  0.507  0.006447   \n",
       "4          4vdJGgZpj4k.003.mp4  0.585  0.616  0.494  0.606  0.587  0.037527   \n",
       "9          nvlqJbHk_Lc.003.mp4  0.511  0.465  0.391  0.444  0.439 -0.094752   \n",
       "2          300gK3CnzW0.001.mp4  0.462  0.413  0.416  0.498  0.431 -0.185699   \n",
       "\n",
       "                 SN        TF        JP  MBTI  MBTI_Score  Match  \n",
       "Person ID                                                         \n",
       "5         -0.027361 -0.100093 -0.049093  ENFP    0.289871   75.0  \n",
       "6         -0.029419 -0.087981 -0.050501  ENFP    0.277057   75.0  \n",
       "10        -0.028874 -0.081294 -0.036454  ENFP    0.268961   75.0  \n",
       "8         -0.025332 -0.090041 -0.042964  ENFP    0.266504   75.0  \n",
       "3          0.068617 -0.278880  0.053384  ISFJ    0.166132   50.0  \n",
       "1          0.002056 -0.092138  0.046539  ESFJ    0.139850   75.0  \n",
       "7          0.037143 -0.271593 -0.105712  ESFP    0.139020   50.0  \n",
       "4          0.002895 -0.081646  0.045425  ESFJ    0.123449   75.0  \n",
       "9         -0.007199 -0.083317 -0.132767  INFP    0.045258   50.0  \n",
       "2          0.017946  0.083205  0.030144  ISTJ    0.007536   25.0  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "_b5._professional_match(\n",
    "    correlation_coefficients = df_correlation_coefficients,\n",
    "    personality_type = \"ENFJ\",\n",
    "    threshold = 0.5,\n",
    "    out = True\n",
    ")\n",
    "\n",
    "_b5._save_logs(df = _b5._df_files_MBTI_job_match, name = 'MBTI_ranking_fi_en', out = True)\n",
    "\n",
    "# Опционно\n",
    "df = _b5.df_files_MBTI_job_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": "2297292e-1e4b-44e0-9c85-ab0fba999892",
   "metadata": {},
   "source": [
    "### `MuPTA` (ru)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "3887d07c-eef2-4980-8d82-cabf6568aa7d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/markdown": [
       "<span style=\"color:#666\">**[</span><span style=\"color:#1776D2\">2024-10-10 17:24:03</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 17:24:04</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": [
       "<div>\n",
       "<style scoped>\n",
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       "        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>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 17:24:04</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": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
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       "    }\n",
       "\n",
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       "        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>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 17:24:04</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: 322.244 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": "193025ae-0e9e-4a91-a3df-8f3bfc395a9d",
   "metadata": {},
   "source": [
    "<hr>\n",
    "\n",
    "Для выполнения ранжирования кандидатов необходимо знать весовые коэффициенты, определяющие приоритетность персональных качеств личности в зависимости от профессии.\n",
    "\n",
    "Предлагаются весовые коэффициенты для 5 профессий, вычисленные на основе научных статей:\n",
    "\n",
    "1) Sajjad H. et al. Personality and Career Choices // African Journal of Business Management. - 2012. – Vol. 6 (6) – pp. 2255-2260.\n",
    "2) Alkhelil A. H. The Relationship between Personality Traits and Career Choice: A Case Study of Secondary School Students // International Journal of Academic Research in Progressive Education and Development. – 2016. – Vol. 5(2). – pp. 2226-6348.\n",
    "3) De Jong N. et al. Personality Traits and Career Role Enactment: Career Role Preferences as a Mediator // Frontiers in Psychology. – 2019. – Vol. 10. – pp. 1720.\n",
    "\n",
    "Пользователь может установить свои весовые коэффициенты; сумма весов должна быть равна 100."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "f79ba8a2-c173-4d83-9cf7-d0c991c7bcc0",
   "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>Profession</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>Managers/executives</td>\n",
       "      <td>15</td>\n",
       "      <td>35</td>\n",
       "      <td>15</td>\n",
       "      <td>30</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Entrepreneurship</td>\n",
       "      <td>30</td>\n",
       "      <td>30</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Social/Non profit making professions</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>35</td>\n",
       "      <td>35</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Public sector professions</td>\n",
       "      <td>15</td>\n",
       "      <td>50</td>\n",
       "      <td>15</td>\n",
       "      <td>15</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Scientists/researchers, and engineers</td>\n",
       "      <td>50</td>\n",
       "      <td>15</td>\n",
       "      <td>5</td>\n",
       "      <td>15</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                               Profession  Openness  Conscientiousness  \\\n",
       "ID                                                                       \n",
       "1                     Managers/executives        15                 35   \n",
       "2                        Entrepreneurship        30                 30   \n",
       "3    Social/Non profit making professions         5                  5   \n",
       "4               Public sector professions        15                 50   \n",
       "5   Scientists/researchers, and engineers        50                 15   \n",
       "\n",
       "    Extraversion  Agreeableness  Non-Neuroticism  \n",
       "ID                                                \n",
       "1             15             30                5  \n",
       "2              5              5               30  \n",
       "3             35             35               20  \n",
       "4             15             15                5  \n",
       "5              5             15               15  "
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Загрузка датафрейма с весовыми коэффициентами\n",
    "url = 'https://download.sberdisk.ru/download/file/478675798?token=fF5fNZVpthQlEV0&filename=traits_priority_for_professions.csv'\n",
    "traits_priority_for_professions = pd.read_csv(url)\n",
    "\n",
    "traits_priority_for_professions.index.name = 'ID'\n",
    "traits_priority_for_professions.index += 1\n",
    "traits_priority_for_professions.index = traits_priority_for_professions.index.map(str)\n",
    "\n",
    "traits_priority_for_professions"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "36fbef26-34ec-4812-9074-6eb6879069d3",
   "metadata": {},
   "source": [
    "#### Ранжирование кандидатов на должность инженера-проектировщика"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "16e43257-3500-4dd8-b04a-34f7435fc185",
   "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>Candidate score</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>0.759</td>\n",
       "      <td>0.713</td>\n",
       "      <td>0.658</td>\n",
       "      <td>0.831</td>\n",
       "      <td>0.508</td>\n",
       "      <td>72.022</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>70.828</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>70.475</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>67.163</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>65.400</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>64.833</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>64.447</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>63.247</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>62.660</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>62.143</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                               Path    OPE    CON    EXT    AGR   NNEU  \\\n",
       "Person ID                                                                \n",
       "10         speaker_27_center_83.mov  0.759  0.713  0.658  0.831  0.508   \n",
       "1          speaker_01_center_83.mov  0.766  0.697  0.656  0.760  0.494   \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",
       "2          speaker_06_center_83.mov  0.687  0.659  0.612  0.750  0.421   \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",
       "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",
       "\n",
       "           Candidate score  \n",
       "Person ID                   \n",
       "10                  72.022  \n",
       "1                   70.828  \n",
       "7                   70.475  \n",
       "8                   67.163  \n",
       "9                   65.400  \n",
       "2                   64.833  \n",
       "5                   64.447  \n",
       "6                   63.247  \n",
       "3                   62.660  \n",
       "4                   62.143  "
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "weights = traits_priority_for_professions.iloc[4].values[1:]\n",
    "weights = list(map(int, weights))\n",
    "\n",
    "_b5._candidate_ranking(\n",
    "    weigths_openness = weights[0], \n",
    "    weigths_conscientiousness = weights[1],\n",
    "    weigths_extraversion = weights[2],\n",
    "    weigths_agreeableness = weights[3], \n",
    "    weigths_non_neuroticism = weights[4],\n",
    "    out = False\n",
    ")\n",
    "\n",
    "_b5._save_logs(df = _b5.df_files_ranking_, name = 'engineer_candidate_ranking_mupta_ru', out = True)\n",
    "\n",
    "# Опционно\n",
    "df = _b5.df_files_ranking_.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": "87ea0221-872f-4ac0-b89a-2c4fcaaa5b45",
   "metadata": {},
   "source": [
    "#### Ранжирование кандидатов на должность менеджера"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "41e60b73-f8a0-444d-a696-2f83ff138e53",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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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>Candidate score</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>0.759</td>\n",
       "      <td>0.713</td>\n",
       "      <td>0.658</td>\n",
       "      <td>0.831</td>\n",
       "      <td>0.508</td>\n",
       "      <td>73.659</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>70.980</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>70.703</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>70.152</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>67.153</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>66.427</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>65.843</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>64.840</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>64.202</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>62.016</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                               Path    OPE    CON    EXT    AGR   NNEU  \\\n",
       "Person ID                                                                \n",
       "10         speaker_27_center_83.mov  0.759  0.713  0.658  0.831  0.508   \n",
       "1          speaker_01_center_83.mov  0.766  0.697  0.656  0.760  0.494   \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",
       "2          speaker_06_center_83.mov  0.687  0.659  0.612  0.750  0.421   \n",
       "9          speaker_24_center_83.mov  0.711  0.663  0.611  0.711  0.414   \n",
       "6          speaker_15_center_83.mov  0.671  0.671  0.602  0.709  0.400   \n",
       "3          speaker_07_center_83.mov  0.672  0.661  0.572  0.705  0.381   \n",
       "5          speaker_11_center_83.mov  0.718  0.599  0.574  0.732  0.380   \n",
       "4          speaker_10_center_83.mov  0.698  0.599  0.572  0.675  0.351   \n",
       "\n",
       "           Candidate score  \n",
       "Person ID                   \n",
       "10                  73.659  \n",
       "1                   70.980  \n",
       "7                   70.703  \n",
       "8                   70.152  \n",
       "2                   67.153  \n",
       "9                   66.427  \n",
       "6                   65.843  \n",
       "3                   64.840  \n",
       "5                   64.202  \n",
       "4                   62.016  "
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "weights = traits_priority_for_professions.iloc[0].values[1:]\n",
    "weights = list(map(int, weights))\n",
    "\n",
    "_b5._candidate_ranking(\n",
    "    weigths_openness = weights[0], \n",
    "    weigths_conscientiousness = weights[1],\n",
    "    weigths_extraversion = weights[2],\n",
    "    weigths_agreeableness = weights[3], \n",
    "    weigths_non_neuroticism = weights[4],\n",
    "    out = False\n",
    ")\n",
    "\n",
    "_b5._save_logs(df = _b5.df_files_ranking_, name = 'executive_candidate_ranking_mupta_ru', out = True)\n",
    "\n",
    "# Опционно\n",
    "df = _b5.df_files_ranking_.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": "6a176cee-f519-4065-a742-593f7c4c1a98",
   "metadata": {},
   "source": [
    "<hr>\n",
    "\n",
    "Для ранжирования кандидатов по профессиональным навыкам необходимо задать по два коэффициента корреляции для каждого персонального качества личности человека и навыка, а также порога полярности качеств. Эти коэффициенты должны показывать, как измениться оценка качества человека если она больше или меньше заданного порога полярности качеств. \n",
    "\n",
    "В качестве примера предлагается использование коэффициентов корреляции между двумя людьми в четырьмя профессиональными навыками, представленных в статье:\n",
    "\n",
    "1) Wehner C., de Grip A., Pfeifer H. Do recruiters select workers with different personality traits for different tasks? A discrete choice experiment // Labour Economics. - 2022. - vol. 78. - pp. 102186.\n",
    "\n",
    "Представлены 4 профессиональных навыка: \n",
    "\n",
    "1) Analytical (Аналитические навыки). Умение эффективно решать новые задачи, требующие глубокого анализа. \n",
    "2) Interactive (Навыки межличностного общения). Умение убеждать и достигать компромиссов с заказчиками и коллегами.\n",
    "4) Routine (Способность выполнять рутинную работу). Умение эффективно управлять рутинными задачами, соблюдая точность и внимание к деталям.\n",
    "5) Non-Routine (Способность выполнять нестандартную работу). Умение реагировать и решать проблемы, не имеющие установленного порядка, проявляя адаптивность и креативные навыки в решении задач.\n",
    "\n",
    "Пользователь может установить свои коэффициенты корреляции и ранжировать кандидатов по другим профессиональным навыкам."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ff810b13-0fae-4201-a74a-79f2f978e1e7",
   "metadata": {},
   "source": [
    "#### Ранжирование кандидатов по профессиональным навыкам"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "1b7cae2f-76d6-4628-acfc-54d2f941113a",
   "metadata": {},
   "outputs": [
    {
     "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>Trait</th>\n",
       "      <th>Score_level</th>\n",
       "      <th>Analytical</th>\n",
       "      <th>Interactive</th>\n",
       "      <th>Routine</th>\n",
       "      <th>Non-Routine</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>Openness</td>\n",
       "      <td>high</td>\n",
       "      <td>0.082</td>\n",
       "      <td>0.348</td>\n",
       "      <td>0.571</td>\n",
       "      <td>0.510</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Openness</td>\n",
       "      <td>low</td>\n",
       "      <td>0.196</td>\n",
       "      <td>0.152</td>\n",
       "      <td>0.148</td>\n",
       "      <td>0.218</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Conscientiousness</td>\n",
       "      <td>high</td>\n",
       "      <td>0.994</td>\n",
       "      <td>1.333</td>\n",
       "      <td>1.507</td>\n",
       "      <td>1.258</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Conscientiousness</td>\n",
       "      <td>low</td>\n",
       "      <td>0.241</td>\n",
       "      <td>0.188</td>\n",
       "      <td>0.191</td>\n",
       "      <td>0.267</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Extraversion</td>\n",
       "      <td>high</td>\n",
       "      <td>0.169</td>\n",
       "      <td>-0.060</td>\n",
       "      <td>0.258</td>\n",
       "      <td>0.017</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Extraversion</td>\n",
       "      <td>low</td>\n",
       "      <td>0.181</td>\n",
       "      <td>0.135</td>\n",
       "      <td>0.130</td>\n",
       "      <td>0.194</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Agreeableness</td>\n",
       "      <td>high</td>\n",
       "      <td>1.239</td>\n",
       "      <td>0.964</td>\n",
       "      <td>1.400</td>\n",
       "      <td>1.191</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Agreeableness</td>\n",
       "      <td>low</td>\n",
       "      <td>0.226</td>\n",
       "      <td>0.180</td>\n",
       "      <td>0.189</td>\n",
       "      <td>0.259</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Non-Neuroticism</td>\n",
       "      <td>high</td>\n",
       "      <td>0.636</td>\n",
       "      <td>0.777</td>\n",
       "      <td>0.876</td>\n",
       "      <td>0.729</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Non-Neuroticism</td>\n",
       "      <td>low</td>\n",
       "      <td>0.207</td>\n",
       "      <td>0.159</td>\n",
       "      <td>0.166</td>\n",
       "      <td>0.238</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                Trait Score_level  Analytical  Interactive  Routine  \\\n",
       "ID                                                                    \n",
       "1            Openness        high       0.082        0.348    0.571   \n",
       "2            Openness         low       0.196        0.152    0.148   \n",
       "3   Conscientiousness        high       0.994        1.333    1.507   \n",
       "4   Conscientiousness         low       0.241        0.188    0.191   \n",
       "5        Extraversion        high       0.169       -0.060    0.258   \n",
       "6        Extraversion         low       0.181        0.135    0.130   \n",
       "7       Agreeableness        high       1.239        0.964    1.400   \n",
       "8       Agreeableness         low       0.226        0.180    0.189   \n",
       "9     Non-Neuroticism        high       0.636        0.777    0.876   \n",
       "10    Non-Neuroticism         low       0.207        0.159    0.166   \n",
       "\n",
       "    Non-Routine  \n",
       "ID               \n",
       "1         0.510  \n",
       "2         0.218  \n",
       "3         1.258  \n",
       "4         0.267  \n",
       "5         0.017  \n",
       "6         0.194  \n",
       "7         1.191  \n",
       "8         0.259  \n",
       "9         0.729  \n",
       "10        0.238  "
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Загрузка датафрейма с коэффициентами корреляции\n",
    "url = 'https://download.sberdisk.ru/download/file/478678231?token=0qiZwliLtHWWYMv&filename=professional_skills.csv'\n",
    "df_professional_skills = pd.read_csv(url)\n",
    "\n",
    "df_professional_skills.index.name = 'ID'\n",
    "df_professional_skills.index += 1\n",
    "df_professional_skills.index = df_professional_skills.index.map(str)\n",
    "\n",
    "df_professional_skills"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "0921b47e-003c-41a6-9fd0-ff1a4b434f70",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "</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>Analytical</th>\n",
       "      <th>Interactive</th>\n",
       "      <th>Routine</th>\n",
       "      <th>Non-Routine</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>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>0.447</td>\n",
       "      <td>0.474</td>\n",
       "      <td>0.657</td>\n",
       "      <td>0.531</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>0.387</td>\n",
       "      <td>0.394</td>\n",
       "      <td>0.559</td>\n",
       "      <td>0.459</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>0.383</td>\n",
       "      <td>0.390</td>\n",
       "      <td>0.559</td>\n",
       "      <td>0.459</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>0.382</td>\n",
       "      <td>0.393</td>\n",
       "      <td>0.560</td>\n",
       "      <td>0.460</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>0.366</td>\n",
       "      <td>0.374</td>\n",
       "      <td>0.533</td>\n",
       "      <td>0.437</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>0.357</td>\n",
       "      <td>0.369</td>\n",
       "      <td>0.525</td>\n",
       "      <td>0.430</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>0.357</td>\n",
       "      <td>0.368</td>\n",
       "      <td>0.522</td>\n",
       "      <td>0.427</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>0.352</td>\n",
       "      <td>0.364</td>\n",
       "      <td>0.515</td>\n",
       "      <td>0.423</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>0.347</td>\n",
       "      <td>0.356</td>\n",
       "      <td>0.510</td>\n",
       "      <td>0.418</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>0.332</td>\n",
       "      <td>0.343</td>\n",
       "      <td>0.490</td>\n",
       "      <td>0.401</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                               Path    OPE    CON    EXT    AGR   NNEU  \\\n",
       "Person ID                                                                \n",
       "10         speaker_27_center_83.mov  0.759  0.713  0.658  0.831  0.508   \n",
       "8          speaker_23_center_83.mov  0.700  0.685  0.617  0.806  0.448   \n",
       "7          speaker_19_center_83.mov  0.767  0.658  0.653  0.801  0.463   \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",
       "9          speaker_24_center_83.mov  0.711  0.663  0.611  0.711  0.414   \n",
       "6          speaker_15_center_83.mov  0.671  0.671  0.602  0.709  0.400   \n",
       "3          speaker_07_center_83.mov  0.672  0.661  0.572  0.705  0.381   \n",
       "5          speaker_11_center_83.mov  0.718  0.599  0.574  0.732  0.380   \n",
       "4          speaker_10_center_83.mov  0.698  0.599  0.572  0.675  0.351   \n",
       "\n",
       "           Analytical  Interactive  Routine  Non-Routine  \n",
       "Person ID                                                 \n",
       "10              0.447        0.474    0.657        0.531  \n",
       "8               0.387        0.394    0.559        0.459  \n",
       "7               0.383        0.390    0.559        0.459  \n",
       "1               0.382        0.393    0.560        0.460  \n",
       "2               0.366        0.374    0.533        0.437  \n",
       "9               0.357        0.369    0.525        0.430  \n",
       "6               0.357        0.368    0.522        0.427  \n",
       "3               0.352        0.364    0.515        0.423  \n",
       "5               0.347        0.356    0.510        0.418  \n",
       "4               0.332        0.343    0.490        0.401  "
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "_b5._priority_skill_calculation(\n",
    "    correlation_coefficients = df_professional_skills,\n",
    "    threshold = 0.5,\n",
    "    out = True\n",
    ")\n",
    "\n",
    "_b5._save_logs(df = _b5.df_files_priority_skill_, name = 'skill_candidate_ranking_mupta_ru', out = True)\n",
    "\n",
    "# Опционно\n",
    "df = _b5.df_files_priority_skill_.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": "f443f6ad",
   "metadata": {},
   "source": [
    "<hr>\n",
    "\n",
    "Для ранжирования кандидатов по одному из шестнадцати типов личности MBTI необходимо задать матрицу корреляции между персональными качествами личности человека и четырьмя диспозициями MBTI, установить порог полярности качеств и указать целевой тип личности MBTI.\n",
    "\n",
    "В качестве примера предлагается использование коэффициентов корреляции, представленных в статье [1]. Описание типов личности MBTI и соотвествующие им успешные профессии представлены в статье [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) Tieger P.D., Barron B., Tieger K. Do what you are: Discover the perfect career for you through the secrets of personality type // Hachette UK. - 2024.\n",
    "\n",
    "##### Типы личности MBTI основаны на четырех измерениях личности:\n",
    "\n",
    "<div style=\"text-align: center;\">\n",
    "  <table style=\"width:100%; margin: 0 auto; font-size: 0.6em;\">\n",
    "    <tr>\n",
    "      <th>Описание измерения</th>\n",
    "      <th>Измерение</th>\n",
    "    </tr>\n",
    "    <tr>\n",
    "      <td>Как мы взаимодействуем с миром и куда направляем свою энергию</td>\n",
    "      <td>(E) Экстраверсия - Интроверсия (I)</td>\n",
    "    </tr>\n",
    "    <tr>\n",
    "      <td>Вид информации, которую мы естественным образом замечаем</td>\n",
    "      <td>(S) Сенсорика - Интуиция (N)</td>\n",
    "    </tr>\n",
    "    <tr>\n",
    "      <td>Как мы принимаем решения</td>\n",
    "      <td>(T) Логика - Чувства (F)</td>\n",
    "    </tr>\n",
    "    <tr>\n",
    "      <td>Предпочитаем ли мы жить более структурированно (принимая решения) или более спонтанно (принимая информацию)</td>\n",
    "      <td>(J) Оценка - Восприятие (P)</td>\n",
    "    </tr>\n",
    "  </table>\n",
    "</div>\n",
    "\n",
    "##### Типы личности MBTI и соотвествующие им успешные профессии:\n",
    "\n",
    "<div style=\"text-align: center;\">\n",
    "  <table style=\"width: 100%; margin: 0 auto; font-size: 0.6em;\">\n",
    "    <tr>\n",
    "      <th>Тип личности</th>\n",
    "      <th>Описание</th>\n",
    "      <th>Успешные профессии</th>\n",
    "    </tr>\n",
    "    <tr>\n",
    "      <td>ISTJ</td>\n",
    "      <td>Этот человек отличается ответственностью, строгостью и педантичностью. Он опирается на объективные факты и склонен к аналитическому мышлению. Приступает к задаче только тогда, когда уверен в своих возможностях и успехе</td>\n",
    "      <td>Инспектор: бухгалтер, аудитор, бюджетный аналитик, финансовый менеджер, разработчик, системный аналитик, библиотекарь и т. д.</td>\n",
    "    </tr>\n",
    "    <tr>\n",
    "      <td>ISFJ</td>\n",
    "      <td>Этот человек склонен к самоанализу и анализу окружающих, легко распознает фальшь и предпочитает сохранять психологическую дистанцию. Он исполнителен, внимателен и готов помогать другим. Его силы и энергия исходят из внутренних ресурсов, и он всегда полагается на собственный опыт</td>\n",
    "      <td>Защитник: медсестра, врач, ветеринар или ветеринарный ассистент, социальный работник, сельскохозяйственный или пищевой ученый, секретарь, водитель и т. д.</td>\n",
    "    </tr>\n",
    "    <tr>\n",
    "      <td>INFJ</td>\n",
    "      <td>О таких людях говорят: «ему можно доверять». Он отличается высокой чувствительностью, уделяет большое внимание межличностным отношениям, умеет давать ценные советы и помогает раскрывать потенциал других. Развитая интуиция не только генерирует множество идей, но и способствует самоорганизации</td>\n",
    "      <td>Советник: психолог, специалист по управлению персоналом, офис-менеджер, специалист по обучению, графический дизайнер и т. д.</td>\n",
    "    </tr>\n",
    "    <tr>\n",
    "      <td>INTJ</td>\n",
    "      <td>Этот человек умеет выделять главное, говорит четко и по существу, придерживается практического подхода. Он стремится постоянно улучшать свою работу и всегда ищет способы сделать задачу еще лучше. Пустые разговоры ему не по душе, поэтому он избегает больших шумных компаний и с трудом заводит новые знакомства</td>\n",
    "      <td>Мастермайнд: аниматор, архитектор, копирайтер, фотограф, тележурналист, видеомонтажер, специалист по бизнес-развитию, исполнительный директор, профессор и т. д.</td>\n",
    "    </tr>\n",
    "    <tr>\n",
    "      <td>ISTP</td>\n",
    "      <td>Этот человек воспринимает мир через ощущения. По природе эмпат, но чаще сосредоточен на себе. Его умение объективно принимать решения и анализировать ситуацию указывает на технический склад ума. Он всегда соблюдает дедлайны, хотя иногда может поступить неожиданно</td>\n",
    "      <td>Создатель: инженер, техник, строитель, инспектор, судебный эксперт, программист, разработчик ПО и т. д.</td>\n",
    "    </tr>\n",
    "    <tr>\n",
    "      <td>ISFP</td>\n",
    "      <td>Этот человек умеет находить радость в однообразии и рутинных делах. Прекрасно ладит с людьми, избегая конфликтов. Ему важно чувствовать свою значимость и оказывать помощь. Такой человек не стремится руководить или менять других, уважает их личные границы и ожидает того же в ответ. По натуре он приземленный практик, на которого всегда можно положиться</td>\n",
    "      <td>Композитор: помощник по маркетингу, танцор, шеф-повар, офис-администратор, художник, дизайнер интерьеров, секретарь, медсестра и т. д.</td>\n",
    "    </tr>\n",
    "    <tr>\n",
    "      <td>INFP</td>\n",
    "      <td>Этот человек - чувствительный лирик, прекрасно разбирающийся в людях и легко вызывающий у них симпатию. Он обладает отличным чувством юмора и уделяет большое внимание своему внешнему виду. Стремится к самопознанию, гармонии с собой и старается быть полезным окружающим</td>\n",
    "      <td>Целитель: писатель, дизайнер мультимедиа, менеджер по работе с клиентами, учитель для детей с особыми потребностями, тренер, редактор, модельер и т. д.</td>\n",
    "    </tr>\n",
    "    <tr>\n",
    "      <td>INTP</td>\n",
    "      <td>Этот человек - эрудит с философским складом ума. Он тщательно анализирует свои решения, стремясь к объективности и беспристрастности. Бурные проявления эмоций ему не свойственны. Однако большое количество данных и их изменчивость могут вызывать у него внутреннее напряжение</td>\n",
    "      <td>Архитектор: технический писатель, веб-разработчик, аналитик информационной безопасности, исследователь, ученый, юрист и т. д.</td>\n",
    "    </tr>\n",
    "    <tr>\n",
    "      <td>ESTP</td>\n",
    "      <td>Этот человек всегда добивается успеха, невзирая на препятствия, которые лишь усиливают его целеустремленность. Он стремится к лидерским позициям и плохо переносит роль подчиненного. Обычно разрабатывает четкий план действий и неуклонно ему следует</td>\n",
    "      <td>Промоутер: специалист по работе с клиентами, актер, личный тренер, бренд-амбассадор, менеджер, предприниматель, креативный директор, полицейский, маркетолог, производитель и т. д.</td>\n",
    "    </tr>\n",
    "    <tr>\n",
    "      <td>ESFP</td>\n",
    "      <td>Этот человек легко выявляет слабые стороны людей, что позволяет ему эффективно манипулировать и управлять. В общении он чаще всего руководствуется собственными интересами и предпочитает жить в настоящем. Часто не завершает начатое, стремясь к быстрым результатам. Однако при этом стремится поддерживать гармоничные отношения с окружающими</td>\n",
    "      <td>Исполнитель: бортпроводник, артист, учитель, менеджер по связям с общественностью, торговый представитель, организатор мероприятий и т. д.</td>\n",
    "    </tr>\n",
    "    <tr>\n",
    "      <td>ENFP</td>\n",
    "      <td>Этот человек - творческая личность и фантазер, обладающий качествами, которые помогают ему успешно взаимодействовать с другими, быть открытым и общительным. Он активно участвует в различных мероприятиях, легко решает возникающие вопросы и демонстрирует гибкость</td>\n",
    "      <td>Чемпион: медицинский работник, продюсер, продавец-консультант, специалист по обслуживанию клиентов, сценарист, ведущий на ТВ/радио и т. д.</td>\n",
    "    </tr>\n",
    "    <tr>\n",
    "      <td>ENTP</td>\n",
    "      <td>Этот человек - изобретательный, инициативный и гибкий. Он генератор идей и первопроходец, который не выносит рутины. Постоянное движение и интуитивное принятие решений всегда сопровождают его в работе</td>\n",
    "      <td>Новатор: инженер, маркетолог, менеджер по социальным сетям, аналитик управления, руководитель цифрового маркетинга, бизнес-консультант, разработчик игр, менеджер по продажам и т. д.</td>\n",
    "    </tr>\n",
    "    <tr>\n",
    "      <td>ESTJ</td>\n",
    "      <td>Это трудолюбивый человек, который воспринимает мир таким, какой он есть. Он склонен тщательно планировать и доводить дела до конца. Заботится о своем ближайшем окружении, проявляет добродушие, но иногда может быть вспыльчивым, резким и упрямым</td>\n",
    "      <td>Супервайзер: управляющий директор, менеджер отеля, финансовый сотрудник, судья, агент по недвижимости, генеральный директор, шеф-повар, менеджер по бизнес-развитию, телемаркетолог и т. д.</td>\n",
    "    </tr>\n",
    "    <tr>\n",
    "      <td>ESFJ</td>\n",
    "      <td>Этот человек умеет оказывать влияние на людей, проявляет заботу и готов жертвовать собой ради других. Он легко устанавливает контакт с любым человеком и способен направить ситуацию в нужное ему русло</td>\n",
    "      <td>Поставщик: специалист по технической поддержке, менеджер по работе с клиентами, профессор колледжа, медицинский исследователь, бухгалтер, фотожурналист и т. д.</td>\n",
    "    </tr>\n",
    "    <tr>\n",
    "      <td>ENFJ</td>\n",
    "      <td>Этот человек отличается эмоциональностью и эмпатией. Его мимика выразительна, а речь — красноречива. Благодаря своей самоорганизованности, он успешно воплощает свои фантазии и идеи в жизнь. Он интуитивно понимает, какое решение следует принять в каждой конкретной ситуации</td>\n",
    "      <td>Учитель: менеджер по связям с общественностью, менеджер по продажам, директор по управлению персоналом, арт-директор, консультант и т. д.</td>\n",
    "    </tr>\n",
    "    <tr>\n",
    "      <td>ENTJ</td>\n",
    "      <td>Этот человек легко увлекается, готов рисковать и полагается на интуицию. Без страха внедряет новые технологии и способен глубоко анализировать как себя, так и окружающий мир. Жизнь для него - это борьба, в которой он чувствует себя уверенно. Открыт для новых возможностей, но при этом нуждается в контроле</td>\n",
    "      <td>Командир: руководитель строительства, администратор службы здравоохранения, финансовый бухгалтер, аудитор, юрист, директор школы, химический инженер, менеджер баз данных и т. д.</td>\n",
    "    </tr>\n",
    "  </table>\n",
    "</div>\n",
    "\n",
    "Пользователь может установить свои коэффициенты корреляции и ранжировать кандидатов по другим типам личности."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "f3ab3951",
   "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": 18,
     "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": 19,
   "id": "3743bc4e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\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>EI</th>\n",
       "      <th>SN</th>\n",
       "      <th>TF</th>\n",
       "      <th>JP</th>\n",
       "      <th>MBTI</th>\n",
       "      <th>MBTI_Score</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",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\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>0.418321</td>\n",
       "      <td>75.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>speaker_01_center_83.mov</td>\n",
       "      <td>0.766</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>0.406929</td>\n",
       "      <td>75.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>speaker_23_center_83.mov</td>\n",
       "      <td>0.700</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>0.396993</td>\n",
       "      <td>75.0</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>0.380647</td>\n",
       "      <td>75.0</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>0.379269</td>\n",
       "      <td>75.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>speaker_24_center_83.mov</td>\n",
       "      <td>0.711</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>0.375154</td>\n",
       "      <td>75.0</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>0.363871</td>\n",
       "      <td>75.0</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>0.361447</td>\n",
       "      <td>75.0</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>0.359650</td>\n",
       "      <td>75.0</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>0.357821</td>\n",
       "      <td>75.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                               Path    OPE    CON    EXT    AGR   NNEU  \\\n",
       "Person ID                                                                \n",
       "7          speaker_19_center_83.mov  0.767  0.658  0.653  0.801  0.463   \n",
       "1          speaker_01_center_83.mov  0.766  0.697  0.656  0.760  0.494   \n",
       "8          speaker_23_center_83.mov  0.700  0.685  0.617  0.806  0.448   \n",
       "2          speaker_06_center_83.mov  0.687  0.659  0.612  0.750  0.421   \n",
       "5          speaker_11_center_83.mov  0.718  0.599  0.574  0.732  0.380   \n",
       "9          speaker_24_center_83.mov  0.711  0.663  0.611  0.711  0.414   \n",
       "6          speaker_15_center_83.mov  0.671  0.671  0.602  0.709  0.400   \n",
       "10         speaker_27_center_83.mov  0.759  0.713  0.658  0.831  0.508   \n",
       "4          speaker_10_center_83.mov  0.698  0.599  0.572  0.675  0.351   \n",
       "3          speaker_07_center_83.mov  0.672  0.661  0.572  0.705  0.381   \n",
       "\n",
       "                 EI        SN        TF        JP  MBTI  MBTI_Score  Match  \n",
       "Person ID                                                                   \n",
       "7          0.205006 -0.028877 -0.323879 -0.052313  ENFP    0.418321   75.0  \n",
       "1          0.203710 -0.032534 -0.306328 -0.048136  ENFP    0.406929   75.0  \n",
       "8          0.194016 -0.026570 -0.308738 -0.035061  ENFP    0.396993   75.0  \n",
       "2          0.191874 -0.027843 -0.287812 -0.037850  ENFP    0.380647   75.0  \n",
       "5          0.187565 -0.026114 -0.292012 -0.049261  ENFP    0.379269   75.0  \n",
       "9          0.193712 -0.030591 -0.275902 -0.042274  ENFP    0.375154   75.0  \n",
       "6          0.189904 -0.029607 -0.265650 -0.034305  ENFP    0.363871   75.0  \n",
       "10         0.285739 -0.029510 -0.166680 -0.042916  ENFP    0.361447   75.0  \n",
       "4          0.186613 -0.028317 -0.264603 -0.047660  ENFP    0.359650   75.0  \n",
       "3          0.184889 -0.028534 -0.263672 -0.033835  ENFP    0.357821   75.0  "
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "_b5._professional_match(\n",
    "    correlation_coefficients = df_correlation_coefficients,\n",
    "    personality_type = \"ENFJ\",\n",
    "    threshold = 0.5,\n",
    "    out = True\n",
    ")\n",
    "\n",
    "_b5._save_logs(df = _b5._df_files_MBTI_job_match, name = 'MBTI_ranking_mupta_ru', out = True)\n",
    "\n",
    "# Опционно\n",
    "df = _b5.df_files_MBTI_job_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": "708fcd2b-eb78-4f58-96d2-19298b8c26d9",
   "metadata": {},
   "source": [
    "### `MuPTA` (en)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "1b1f1294-6f09-4827-85c6-75ccc7fbd375",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/markdown": [
       "<span style=\"color:#666\">**[</span><span style=\"color:#1776D2\">2024-10-10 17:40:54</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 17:40:55</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": [
       "<div>\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>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 17:40:55</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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       "</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 17:40:55</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: 316.051 сек. ---**</span>"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 21,
     "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_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": "b7d2dceb-423d-463d-ba89-61603250689a",
   "metadata": {},
   "source": [
    "<hr>\n",
    "\n",
    "Для выполнения ранжирования кандидатов необходимо знать весовые коэффициенты, определяющие приоритетность персональных качеств личности в зависимости от профессии.\n",
    "\n",
    "Предлагаются весовые коэффициенты для 5 профессий, вычисленные на основе научных статей:\n",
    "\n",
    "1) Sajjad H. et al. Personality and Career Choices // African Journal of Business Management. - 2012. – Vol. 6 (6) – pp. 2255-2260.\n",
    "2) Alkhelil A. H. The Relationship between Personality Traits and Career Choice: A Case Study of Secondary School Students // International Journal of Academic Research in Progressive Education and Development. – 2016. – Vol. 5(2). – pp. 2226-6348.\n",
    "3) De Jong N. et al. Personality Traits and Career Role Enactment: Career Role Preferences as a Mediator // Frontiers in Psychology. – 2019. – Vol. 10. – pp. 1720.\n",
    "\n",
    "Пользователь может установить свои весовые коэффициенты; сумма весов должна быть равна 100."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "b38c6569-1558-447a-875d-5735451e8f26",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Profession</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",
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       "      <th></th>\n",
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       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Managers/executives</td>\n",
       "      <td>15</td>\n",
       "      <td>35</td>\n",
       "      <td>15</td>\n",
       "      <td>30</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Entrepreneurship</td>\n",
       "      <td>30</td>\n",
       "      <td>30</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Social/Non profit making professions</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>35</td>\n",
       "      <td>35</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Public sector professions</td>\n",
       "      <td>15</td>\n",
       "      <td>50</td>\n",
       "      <td>15</td>\n",
       "      <td>15</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Scientists/researchers, and engineers</td>\n",
       "      <td>50</td>\n",
       "      <td>15</td>\n",
       "      <td>5</td>\n",
       "      <td>15</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                               Profession  Openness  Conscientiousness  \\\n",
       "ID                                                                       \n",
       "1                     Managers/executives        15                 35   \n",
       "2                        Entrepreneurship        30                 30   \n",
       "3    Social/Non profit making professions         5                  5   \n",
       "4               Public sector professions        15                 50   \n",
       "5   Scientists/researchers, and engineers        50                 15   \n",
       "\n",
       "    Extraversion  Agreeableness  Non-Neuroticism  \n",
       "ID                                                \n",
       "1             15             30                5  \n",
       "2              5              5               30  \n",
       "3             35             35               20  \n",
       "4             15             15                5  \n",
       "5              5             15               15  "
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Загрузка датафрейма с весовыми коэффициентами\n",
    "url = 'https://download.sberdisk.ru/download/file/478675798?token=fF5fNZVpthQlEV0&filename=traits_priority_for_professions.csv'\n",
    "traits_priority_for_professions = pd.read_csv(url)\n",
    "\n",
    "traits_priority_for_professions.index.name = 'ID'\n",
    "traits_priority_for_professions.index += 1\n",
    "traits_priority_for_professions.index = traits_priority_for_professions.index.map(str)\n",
    "\n",
    "traits_priority_for_professions"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ece309a9-5818-435d-8721-b15e2eca8e01",
   "metadata": {},
   "source": [
    "#### Ранжирование кандидатов на должность инженера-проектировщика"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "6d095685-fda4-4943-9a0f-d0463a39798c",
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
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       "      <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>Candidate score</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Person ID</th>\n",
       "      <th></th>\n",
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       "  <tbody>\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>65.521</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>57.934</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>56.914</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>56.704</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>48.093</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>47.873</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>46.254</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>43.708</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>42.671</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>39.015</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                               Path    OPE    CON    EXT    AGR   NNEU  \\\n",
       "Person ID                                                                \n",
       "2          speaker_06_center_83.mov  0.661  0.674  0.603  0.645  0.643   \n",
       "10         speaker_27_center_83.mov  0.566  0.659  0.434  0.591  0.579   \n",
       "6          speaker_15_center_83.mov  0.582  0.562  0.505  0.602  0.522   \n",
       "1          speaker_01_center_83.mov  0.596  0.543  0.441  0.590  0.515   \n",
       "8          speaker_23_center_83.mov  0.501  0.541  0.309  0.441  0.452   \n",
       "7          speaker_19_center_83.mov  0.510  0.448  0.426  0.452  0.448   \n",
       "4          speaker_10_center_83.mov  0.477  0.503  0.374  0.441  0.425   \n",
       "9          speaker_24_center_83.mov  0.428  0.511  0.301  0.434  0.442   \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",
       "\n",
       "           Candidate score  \n",
       "Person ID                   \n",
       "2                   65.521  \n",
       "10                  57.934  \n",
       "6                   56.914  \n",
       "1                   56.704  \n",
       "8                   48.093  \n",
       "7                   47.873  \n",
       "4                   46.254  \n",
       "9                   43.708  \n",
       "3                   42.671  \n",
       "5                   39.015  "
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "weights = traits_priority_for_professions.iloc[4].values[1:]\n",
    "weights = list(map(int, weights))\n",
    "\n",
    "_b5._candidate_ranking(\n",
    "    weigths_openness = weights[0], \n",
    "    weigths_conscientiousness = weights[1],\n",
    "    weigths_extraversion = weights[2],\n",
    "    weigths_agreeableness = weights[3], \n",
    "    weigths_non_neuroticism = weights[4],\n",
    "    out = False\n",
    ")\n",
    "\n",
    "_b5._save_logs(df = _b5.df_files_ranking_, name = 'engineer_candidate_ranking_mupta_en', out = True)\n",
    "\n",
    "# Опционно\n",
    "df = _b5.df_files_ranking_.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": "d56b0166-8fe2-4995-87d7-97b123176e0c",
   "metadata": {},
   "source": [
    "#### Ранжирование кандидатов на должность менеджера"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "5f54009f-1bc5-49cf-8f33-0e99b4307722",
   "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>Candidate score</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>0.661</td>\n",
       "      <td>0.674</td>\n",
       "      <td>0.603</td>\n",
       "      <td>0.645</td>\n",
       "      <td>0.643</td>\n",
       "      <td>65.136</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>58.710</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>56.649</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>54.818</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>46.581</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>45.717</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>45.532</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>44.069</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>41.800</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>37.450</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                               Path    OPE    CON    EXT    AGR   NNEU  \\\n",
       "Person ID                                                                \n",
       "2          speaker_06_center_83.mov  0.661  0.674  0.603  0.645  0.643   \n",
       "10         speaker_27_center_83.mov  0.566  0.659  0.434  0.591  0.579   \n",
       "6          speaker_15_center_83.mov  0.582  0.562  0.505  0.602  0.522   \n",
       "1          speaker_01_center_83.mov  0.596  0.543  0.441  0.590  0.515   \n",
       "8          speaker_23_center_83.mov  0.501  0.541  0.309  0.441  0.452   \n",
       "4          speaker_10_center_83.mov  0.477  0.503  0.374  0.441  0.425   \n",
       "7          speaker_19_center_83.mov  0.510  0.448  0.426  0.452  0.448   \n",
       "9          speaker_24_center_83.mov  0.428  0.511  0.301  0.434  0.442   \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",
       "\n",
       "           Candidate score  \n",
       "Person ID                   \n",
       "2                   65.136  \n",
       "10                  58.710  \n",
       "6                   56.649  \n",
       "1                   54.818  \n",
       "8                   46.581  \n",
       "4                   45.717  \n",
       "7                   45.532  \n",
       "9                   44.069  \n",
       "3                   41.800  \n",
       "5                   37.450  "
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "weights = traits_priority_for_professions.iloc[0].values[1:]\n",
    "weights = list(map(int, weights))\n",
    "\n",
    "_b5._candidate_ranking(\n",
    "    weigths_openness = weights[0], \n",
    "    weigths_conscientiousness = weights[1],\n",
    "    weigths_extraversion = weights[2],\n",
    "    weigths_agreeableness = weights[3], \n",
    "    weigths_non_neuroticism = weights[4],\n",
    "    out = False\n",
    ")\n",
    "\n",
    "_b5._save_logs(df = _b5.df_files_ranking_, name = 'executive_candidate_ranking_mupta_en', out = True)\n",
    "\n",
    "# Опционно\n",
    "df = _b5.df_files_ranking_.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": "b217246c-1cfd-4470-a4df-b335d6f2241e",
   "metadata": {},
   "source": [
    "<hr>\n",
    "\n",
    "Для ранжирования кандидатов по профессиональным навыкам необходимо задать по два коэффициента корреляции для каждого персонального качества личности человека и навыка, а также порога полярности качеств. Эти коэффициенты должны показывать, как измениться оценка качества человека если она больше или меньше заданного порога полярности качеств. \n",
    "\n",
    "В качестве примера предлагается использование коэффициентов корреляции между двумя людьми в четырьмя профессиональными навыками, представленных в статье:\n",
    "\n",
    "1) Wehner C., de Grip A., Pfeifer H. Do recruiters select workers with different personality traits for different tasks? A discrete choice experiment // Labour Economics. - 2022. - vol. 78. - pp. 102186.\n",
    "\n",
    "Представлены 4 профессиональных навыка: \n",
    "\n",
    "1) Analytical (Аналитические навыки). Умение эффективно решать новые задачи, требующие глубокого анализа. \n",
    "2) Interactive (Навыки межличностного общения). Умение убеждать и достигать компромиссов с заказчиками и коллегами.\n",
    "4) Routine (Способность выполнять рутинную работу). Умение эффективно управлять рутинными задачами, соблюдая точность и внимание к деталям.\n",
    "5) Non-Routine (Способность выполнять нестандартную работу). Умение реагировать и решать проблемы, не имеющие установленного порядка, проявляя адаптивность и креативные навыки в решении задач.\n",
    "\n",
    "Пользователь может установить свои коэффициенты корреляции и ранжировать кандидатов по другим профессиональным навыкам."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2b7fc489-957d-4a6f-af41-e9ba012c1e9d",
   "metadata": {},
   "source": [
    "#### Ранжирование кандидатов по профессиональным навыкам"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "2424f29a-3270-4b98-b173-51314c769e2e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
       "    .dataframe thead th {\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>Trait</th>\n",
       "      <th>Score_level</th>\n",
       "      <th>Analytical</th>\n",
       "      <th>Interactive</th>\n",
       "      <th>Routine</th>\n",
       "      <th>Non-Routine</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>Openness</td>\n",
       "      <td>high</td>\n",
       "      <td>0.082</td>\n",
       "      <td>0.348</td>\n",
       "      <td>0.571</td>\n",
       "      <td>0.510</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Openness</td>\n",
       "      <td>low</td>\n",
       "      <td>0.196</td>\n",
       "      <td>0.152</td>\n",
       "      <td>0.148</td>\n",
       "      <td>0.218</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Conscientiousness</td>\n",
       "      <td>high</td>\n",
       "      <td>0.994</td>\n",
       "      <td>1.333</td>\n",
       "      <td>1.507</td>\n",
       "      <td>1.258</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Conscientiousness</td>\n",
       "      <td>low</td>\n",
       "      <td>0.241</td>\n",
       "      <td>0.188</td>\n",
       "      <td>0.191</td>\n",
       "      <td>0.267</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Extraversion</td>\n",
       "      <td>high</td>\n",
       "      <td>0.169</td>\n",
       "      <td>-0.060</td>\n",
       "      <td>0.258</td>\n",
       "      <td>0.017</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Extraversion</td>\n",
       "      <td>low</td>\n",
       "      <td>0.181</td>\n",
       "      <td>0.135</td>\n",
       "      <td>0.130</td>\n",
       "      <td>0.194</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Agreeableness</td>\n",
       "      <td>high</td>\n",
       "      <td>1.239</td>\n",
       "      <td>0.964</td>\n",
       "      <td>1.400</td>\n",
       "      <td>1.191</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Agreeableness</td>\n",
       "      <td>low</td>\n",
       "      <td>0.226</td>\n",
       "      <td>0.180</td>\n",
       "      <td>0.189</td>\n",
       "      <td>0.259</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Non-Neuroticism</td>\n",
       "      <td>high</td>\n",
       "      <td>0.636</td>\n",
       "      <td>0.777</td>\n",
       "      <td>0.876</td>\n",
       "      <td>0.729</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Non-Neuroticism</td>\n",
       "      <td>low</td>\n",
       "      <td>0.207</td>\n",
       "      <td>0.159</td>\n",
       "      <td>0.166</td>\n",
       "      <td>0.238</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                Trait Score_level  Analytical  Interactive  Routine  \\\n",
       "ID                                                                    \n",
       "1            Openness        high       0.082        0.348    0.571   \n",
       "2            Openness         low       0.196        0.152    0.148   \n",
       "3   Conscientiousness        high       0.994        1.333    1.507   \n",
       "4   Conscientiousness         low       0.241        0.188    0.191   \n",
       "5        Extraversion        high       0.169       -0.060    0.258   \n",
       "6        Extraversion         low       0.181        0.135    0.130   \n",
       "7       Agreeableness        high       1.239        0.964    1.400   \n",
       "8       Agreeableness         low       0.226        0.180    0.189   \n",
       "9     Non-Neuroticism        high       0.636        0.777    0.876   \n",
       "10    Non-Neuroticism         low       0.207        0.159    0.166   \n",
       "\n",
       "    Non-Routine  \n",
       "ID               \n",
       "1         0.510  \n",
       "2         0.218  \n",
       "3         1.258  \n",
       "4         0.267  \n",
       "5         0.017  \n",
       "6         0.194  \n",
       "7         1.191  \n",
       "8         0.259  \n",
       "9         0.729  \n",
       "10        0.238  "
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Загрузка датафрейма с коэффициентами корреляции\n",
    "url = 'https://download.sberdisk.ru/download/file/478678231?token=0qiZwliLtHWWYMv&filename=professional_skills.csv'\n",
    "df_professional_skills = pd.read_csv(url)\n",
    "\n",
    "df_professional_skills.index.name = 'ID'\n",
    "df_professional_skills.index += 1\n",
    "df_professional_skills.index = df_professional_skills.index.map(str)\n",
    "\n",
    "df_professional_skills"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "80a54d9a-6f3c-400b-8cdd-d4ddcde2afc2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "        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>Analytical</th>\n",
       "      <th>Interactive</th>\n",
       "      <th>Routine</th>\n",
       "      <th>Non-Routine</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>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>0.407</td>\n",
       "      <td>0.443</td>\n",
       "      <td>0.603</td>\n",
       "      <td>0.487</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>0.376</td>\n",
       "      <td>0.431</td>\n",
       "      <td>0.542</td>\n",
       "      <td>0.466</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>0.354</td>\n",
       "      <td>0.382</td>\n",
       "      <td>0.522</td>\n",
       "      <td>0.422</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>0.345</td>\n",
       "      <td>0.392</td>\n",
       "      <td>0.499</td>\n",
       "      <td>0.430</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>0.170</td>\n",
       "      <td>0.188</td>\n",
       "      <td>0.206</td>\n",
       "      <td>0.205</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>0.167</td>\n",
       "      <td>0.187</td>\n",
       "      <td>0.206</td>\n",
       "      <td>0.203</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>0.166</td>\n",
       "      <td>0.218</td>\n",
       "      <td>0.260</td>\n",
       "      <td>0.244</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>0.085</td>\n",
       "      <td>0.066</td>\n",
       "      <td>0.067</td>\n",
       "      <td>0.096</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>0.084</td>\n",
       "      <td>0.094</td>\n",
       "      <td>0.118</td>\n",
       "      <td>0.137</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>0.079</td>\n",
       "      <td>0.061</td>\n",
       "      <td>0.062</td>\n",
       "      <td>0.088</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                               Path    OPE    CON    EXT    AGR   NNEU  \\\n",
       "Person ID                                                                \n",
       "2          speaker_06_center_83.mov  0.661  0.674  0.603  0.645  0.643   \n",
       "10         speaker_27_center_83.mov  0.566  0.659  0.434  0.591  0.579   \n",
       "6          speaker_15_center_83.mov  0.582  0.562  0.505  0.602  0.522   \n",
       "1          speaker_01_center_83.mov  0.596  0.543  0.441  0.590  0.515   \n",
       "4          speaker_10_center_83.mov  0.477  0.503  0.374  0.441  0.425   \n",
       "9          speaker_24_center_83.mov  0.428  0.511  0.301  0.434  0.442   \n",
       "8          speaker_23_center_83.mov  0.501  0.541  0.309  0.441  0.452   \n",
       "3          speaker_07_center_83.mov  0.440  0.465  0.285  0.423  0.396   \n",
       "7          speaker_19_center_83.mov  0.510  0.448  0.426  0.452  0.448   \n",
       "5          speaker_11_center_83.mov  0.403  0.344  0.317  0.422  0.384   \n",
       "\n",
       "           Analytical  Interactive  Routine  Non-Routine  \n",
       "Person ID                                                 \n",
       "2               0.407        0.443    0.603        0.487  \n",
       "10              0.376        0.431    0.542        0.466  \n",
       "6               0.354        0.382    0.522        0.422  \n",
       "1               0.345        0.392    0.499        0.430  \n",
       "4               0.170        0.188    0.206        0.205  \n",
       "9               0.167        0.187    0.206        0.203  \n",
       "8               0.166        0.218    0.260        0.244  \n",
       "3               0.085        0.066    0.067        0.096  \n",
       "7               0.084        0.094    0.118        0.137  \n",
       "5               0.079        0.061    0.062        0.088  "
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "_b5._priority_skill_calculation(\n",
    "    correlation_coefficients = df_professional_skills,\n",
    "    threshold = 0.5,\n",
    "    out = True\n",
    ")\n",
    "\n",
    "_b5._save_logs(df = _b5.df_files_priority_skill_, name = 'skill_candidate_ranking_mupta_en', out = True)\n",
    "\n",
    "# Опционно\n",
    "df = _b5.df_files_priority_skill_.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": "624ef336",
   "metadata": {},
   "source": [
    "<hr>\n",
    "\n",
    "Для ранжирования кандидатов по одному из шестнадцати типов личности MBTI необходимо задать матрицу корреляции между персональными качествами личности человека и четырьмя диспозициями MBTI, установить порог полярности качеств и указать целевой тип личности MBTI.\n",
    "\n",
    "В качестве примера предлагается использование коэффициентов корреляции, представленных в статье [1]. Описание типов личности MBTI и соотвествующие им успешные профессии представлены в статье [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) Tieger P.D., Barron B., Tieger K. Do what you are: Discover the perfect career for you through the secrets of personality type // Hachette UK. - 2024.\n",
    "\n",
    "##### Типы личности MBTI основаны на четырех измерениях личности:\n",
    "\n",
    "<div style=\"text-align: center;\">\n",
    "  <table style=\"width:100%; margin: 0 auto; font-size: 0.6em;\">\n",
    "    <tr>\n",
    "      <th>Описание измерения</th>\n",
    "      <th>Измерение</th>\n",
    "    </tr>\n",
    "    <tr>\n",
    "      <td>Как мы взаимодействуем с миром и куда направляем свою энергию</td>\n",
    "      <td>(E) Экстраверсия - Интроверсия (I)</td>\n",
    "    </tr>\n",
    "    <tr>\n",
    "      <td>Вид информации, которую мы естественным образом замечаем</td>\n",
    "      <td>(S) Сенсорика - Интуиция (N)</td>\n",
    "    </tr>\n",
    "    <tr>\n",
    "      <td>Как мы принимаем решения</td>\n",
    "      <td>(T) Логика - Чувства (F)</td>\n",
    "    </tr>\n",
    "    <tr>\n",
    "      <td>Предпочитаем ли мы жить более структурированно (принимая решения) или более спонтанно (принимая информацию)</td>\n",
    "      <td>(J) Оценка - Восприятие (P)</td>\n",
    "    </tr>\n",
    "  </table>\n",
    "</div>\n",
    "\n",
    "##### Типы личности MBTI и соотвествующие им успешные профессии:\n",
    "\n",
    "<div style=\"text-align: center;\">\n",
    "  <table style=\"width: 100%; margin: 0 auto; font-size: 0.6em;\">\n",
    "    <tr>\n",
    "      <th>Тип личности</th>\n",
    "      <th>Описание</th>\n",
    "      <th>Успешные профессии</th>\n",
    "    </tr>\n",
    "    <tr>\n",
    "      <td>ISTJ</td>\n",
    "      <td>Этот человек отличается ответственностью, строгостью и педантичностью. Он опирается на объективные факты и склонен к аналитическому мышлению. Приступает к задаче только тогда, когда уверен в своих возможностях и успехе</td>\n",
    "      <td>Инспектор: бухгалтер, аудитор, бюджетный аналитик, финансовый менеджер, разработчик, системный аналитик, библиотекарь и т. д.</td>\n",
    "    </tr>\n",
    "    <tr>\n",
    "      <td>ISFJ</td>\n",
    "      <td>Этот человек склонен к самоанализу и анализу окружающих, легко распознает фальшь и предпочитает сохранять психологическую дистанцию. Он исполнителен, внимателен и готов помогать другим. Его силы и энергия исходят из внутренних ресурсов, и он всегда полагается на собственный опыт</td>\n",
    "      <td>Защитник: медсестра, врач, ветеринар или ветеринарный ассистент, социальный работник, сельскохозяйственный или пищевой ученый, секретарь, водитель и т. д.</td>\n",
    "    </tr>\n",
    "    <tr>\n",
    "      <td>INFJ</td>\n",
    "      <td>О таких людях говорят: «ему можно доверять». Он отличается высокой чувствительностью, уделяет большое внимание межличностным отношениям, умеет давать ценные советы и помогает раскрывать потенциал других. Развитая интуиция не только генерирует множество идей, но и способствует самоорганизации</td>\n",
    "      <td>Советник: психолог, специалист по управлению персоналом, офис-менеджер, специалист по обучению, графический дизайнер и т. д.</td>\n",
    "    </tr>\n",
    "    <tr>\n",
    "      <td>INTJ</td>\n",
    "      <td>Этот человек умеет выделять главное, говорит четко и по существу, придерживается практического подхода. Он стремится постоянно улучшать свою работу и всегда ищет способы сделать задачу еще лучше. Пустые разговоры ему не по душе, поэтому он избегает больших шумных компаний и с трудом заводит новые знакомства</td>\n",
    "      <td>Мастермайнд: аниматор, архитектор, копирайтер, фотограф, тележурналист, видеомонтажер, специалист по бизнес-развитию, исполнительный директор, профессор и т. д.</td>\n",
    "    </tr>\n",
    "    <tr>\n",
    "      <td>ISTP</td>\n",
    "      <td>Этот человек воспринимает мир через ощущения. По природе эмпат, но чаще сосредоточен на себе. Его умение объективно принимать решения и анализировать ситуацию указывает на технический склад ума. Он всегда соблюдает дедлайны, хотя иногда может поступить неожиданно</td>\n",
    "      <td>Создатель: инженер, техник, строитель, инспектор, судебный эксперт, программист, разработчик ПО и т. д.</td>\n",
    "    </tr>\n",
    "    <tr>\n",
    "      <td>ISFP</td>\n",
    "      <td>Этот человек умеет находить радость в однообразии и рутинных делах. Прекрасно ладит с людьми, избегая конфликтов. Ему важно чувствовать свою значимость и оказывать помощь. Такой человек не стремится руководить или менять других, уважает их личные границы и ожидает того же в ответ. По натуре он приземленный практик, на которого всегда можно положиться</td>\n",
    "      <td>Композитор: помощник по маркетингу, танцор, шеф-повар, офис-администратор, художник, дизайнер интерьеров, секретарь, медсестра и т. д.</td>\n",
    "    </tr>\n",
    "    <tr>\n",
    "      <td>INFP</td>\n",
    "      <td>Этот человек - чувствительный лирик, прекрасно разбирающийся в людях и легко вызывающий у них симпатию. Он обладает отличным чувством юмора и уделяет большое внимание своему внешнему виду. Стремится к самопознанию, гармонии с собой и старается быть полезным окружающим</td>\n",
    "      <td>Целитель: писатель, дизайнер мультимедиа, менеджер по работе с клиентами, учитель для детей с особыми потребностями, тренер, редактор, модельер и т. д.</td>\n",
    "    </tr>\n",
    "    <tr>\n",
    "      <td>INTP</td>\n",
    "      <td>Этот человек - эрудит с философским складом ума. Он тщательно анализирует свои решения, стремясь к объективности и беспристрастности. Бурные проявления эмоций ему не свойственны. Однако большое количество данных и их изменчивость могут вызывать у него внутреннее напряжение</td>\n",
    "      <td>Архитектор: технический писатель, веб-разработчик, аналитик информационной безопасности, исследователь, ученый, юрист и т. д.</td>\n",
    "    </tr>\n",
    "    <tr>\n",
    "      <td>ESTP</td>\n",
    "      <td>Этот человек всегда добивается успеха, невзирая на препятствия, которые лишь усиливают его целеустремленность. Он стремится к лидерским позициям и плохо переносит роль подчиненного. Обычно разрабатывает четкий план действий и неуклонно ему следует</td>\n",
    "      <td>Промоутер: специалист по работе с клиентами, актер, личный тренер, бренд-амбассадор, менеджер, предприниматель, креативный директор, полицейский, маркетолог, производитель и т. д.</td>\n",
    "    </tr>\n",
    "    <tr>\n",
    "      <td>ESFP</td>\n",
    "      <td>Этот человек легко выявляет слабые стороны людей, что позволяет ему эффективно манипулировать и управлять. В общении он чаще всего руководствуется собственными интересами и предпочитает жить в настоящем. Часто не завершает начатое, стремясь к быстрым результатам. Однако при этом стремится поддерживать гармоничные отношения с окружающими</td>\n",
    "      <td>Исполнитель: бортпроводник, артист, учитель, менеджер по связям с общественностью, торговый представитель, организатор мероприятий и т. д.</td>\n",
    "    </tr>\n",
    "    <tr>\n",
    "      <td>ENFP</td>\n",
    "      <td>Этот человек - творческая личность и фантазер, обладающий качествами, которые помогают ему успешно взаимодействовать с другими, быть открытым и общительным. Он активно участвует в различных мероприятиях, легко решает возникающие вопросы и демонстрирует гибкость</td>\n",
    "      <td>Чемпион: медицинский работник, продюсер, продавец-консультант, специалист по обслуживанию клиентов, сценарист, ведущий на ТВ/радио и т. д.</td>\n",
    "    </tr>\n",
    "    <tr>\n",
    "      <td>ENTP</td>\n",
    "      <td>Этот человек - изобретательный, инициативный и гибкий. Он генератор идей и первопроходец, который не выносит рутины. Постоянное движение и интуитивное принятие решений всегда сопровождают его в работе</td>\n",
    "      <td>Новатор: инженер, маркетолог, менеджер по социальным сетям, аналитик управления, руководитель цифрового маркетинга, бизнес-консультант, разработчик игр, менеджер по продажам и т. д.</td>\n",
    "    </tr>\n",
    "    <tr>\n",
    "      <td>ESTJ</td>\n",
    "      <td>Это трудолюбивый человек, который воспринимает мир таким, какой он есть. Он склонен тщательно планировать и доводить дела до конца. Заботится о своем ближайшем окружении, проявляет добродушие, но иногда может быть вспыльчивым, резким и упрямым</td>\n",
    "      <td>Супервайзер: управляющий директор, менеджер отеля, финансовый сотрудник, судья, агент по недвижимости, генеральный директор, шеф-повар, менеджер по бизнес-развитию, телемаркетолог и т. д.</td>\n",
    "    </tr>\n",
    "    <tr>\n",
    "      <td>ESFJ</td>\n",
    "      <td>Этот человек умеет оказывать влияние на людей, проявляет заботу и готов жертвовать собой ради других. Он легко устанавливает контакт с любым человеком и способен направить ситуацию в нужное ему русло</td>\n",
    "      <td>Поставщик: специалист по технической поддержке, менеджер по работе с клиентами, профессор колледжа, медицинский исследователь, бухгалтер, фотожурналист и т. д.</td>\n",
    "    </tr>\n",
    "    <tr>\n",
    "      <td>ENFJ</td>\n",
    "      <td>Этот человек отличается эмоциональностью и эмпатией. Его мимика выразительна, а речь — красноречива. Благодаря своей самоорганизованности, он успешно воплощает свои фантазии и идеи в жизнь. Он интуитивно понимает, какое решение следует принять в каждой конкретной ситуации</td>\n",
    "      <td>Учитель: менеджер по связям с общественностью, менеджер по продажам, директор по управлению персоналом, арт-директор, консультант и т. д.</td>\n",
    "    </tr>\n",
    "    <tr>\n",
    "      <td>ENTJ</td>\n",
    "      <td>Этот человек легко увлекается, готов рисковать и полагается на интуицию. Без страха внедряет новые технологии и способен глубоко анализировать как себя, так и окружающий мир. Жизнь для него - это борьба, в которой он чувствует себя уверенно. Открыт для новых возможностей, но при этом нуждается в контроле</td>\n",
    "      <td>Командир: руководитель строительства, администратор службы здравоохранения, финансовый бухгалтер, аудитор, юрист, директор школы, химический инженер, менеджер баз данных и т. д.</td>\n",
    "    </tr>\n",
    "  </table>\n",
    "</div>\n",
    "\n",
    "Пользователь может установить свои коэффициенты корреляции и ранжировать кандидатов по другим типам личности."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "fd23c41d",
   "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": 27,
     "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": 30,
   "id": "69eb410d",
   "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>MBTI_Score</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",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>speaker_06_center_83.mov</td>\n",
       "      <td>0.661</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>0.284152</td>\n",
       "      <td>75.0</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>0.259311</td>\n",
       "      <td>75.0</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>0.165309</td>\n",
       "      <td>100.0</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>0.129477</td>\n",
       "      <td>75.0</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>0.088133</td>\n",
       "      <td>50.0</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>0.084185</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>0.043061</td>\n",
       "      <td>50.0</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>0.026772</td>\n",
       "      <td>50.0</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>0.006465</td>\n",
       "      <td>25.0</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>-0.155235</td>\n",
       "      <td>0.019207</td>\n",
       "      <td>0.050250</td>\n",
       "      <td>0.012514</td>\n",
       "      <td>ISTJ</td>\n",
       "      <td>0.003128</td>\n",
       "      <td>25.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                               Path    OPE    CON    EXT    AGR   NNEU  \\\n",
       "Person ID                                                                \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",
       "10         speaker_27_center_83.mov  0.566  0.659  0.434  0.591  0.579   \n",
       "1          speaker_01_center_83.mov  0.596  0.543  0.441  0.590  0.515   \n",
       "4          speaker_10_center_83.mov  0.477  0.503  0.374  0.441  0.425   \n",
       "9          speaker_24_center_83.mov  0.428  0.511  0.301  0.434  0.442   \n",
       "7          speaker_19_center_83.mov  0.510  0.448  0.426  0.452  0.448   \n",
       "8          speaker_23_center_83.mov  0.501  0.541  0.309  0.441  0.452   \n",
       "5          speaker_11_center_83.mov  0.403  0.344  0.317  0.422  0.384   \n",
       "3          speaker_07_center_83.mov  0.440  0.465  0.285  0.423  0.396   \n",
       "\n",
       "                 EI        SN        TF        JP  MBTI  MBTI_Score  Match  \n",
       "Person ID                                                                   \n",
       "2          0.271478 -0.032624 -0.074766 -0.034321  ENFP    0.284152   75.0  \n",
       "6          0.229601 -0.024972 -0.091174 -0.031648  ENFP    0.259311   75.0  \n",
       "10         0.048690 -0.000776 -0.066064  0.049778  ENFJ    0.165309  100.0  \n",
       "1          0.040210  0.003122 -0.103169  0.029258  ESFJ    0.129477   75.0  \n",
       "4         -0.140376 -0.016646  0.250115  0.159620  INTJ    0.088133   50.0  \n",
       "9         -0.122322 -0.020306  0.240365  0.148065  INTJ    0.084185   50.0  \n",
       "7         -0.101987 -0.007200 -0.078923 -0.128220  INFP    0.043061   50.0  \n",
       "8         -0.040020 -0.049474  0.117143  0.004070  INTJ    0.026772   50.0  \n",
       "5         -0.152724  0.014281  0.070700  0.025861  ISTJ    0.006465   25.0  \n",
       "3         -0.155235  0.019207  0.050250  0.012514  ISTJ    0.003128   25.0  "
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "_b5._professional_match(\n",
    "    correlation_coefficients = df_correlation_coefficients,\n",
    "    personality_type = \"ENFJ\",\n",
    "    threshold = 0.5,\n",
    "    out = True\n",
    ")\n",
    "\n",
    "_b5._save_logs(df = _b5._df_files_MBTI_job_match, name = 'MBTI_ranking_mupta_en', out = True)\n",
    "\n",
    "# Опционно\n",
    "df = _b5.df_files_MBTI_job_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"
   ]
  }
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