docs/source/user_guide/notebooks/Pipeline_practical_task_2.ipynb

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   "source": [
    "## Решение практической задачи 2\n",
    "\n",
    "### Задача: прогнозирование потребительских предпочтений на промышленные товары\n",
    "\n",
    "> Решение практической задачи выполняется в два этапа. На первом этапе необходимо использовать библиотеку OCEAN-AI для получения гипотез предсказаний (оценок персональных качеств личности человека). На втором этапе следует использовать метод _priority_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 инструмент для анализа персональных качеств личности потребителей, что полезно для предсказания того, что их заинтересует. Это позволит компаниям более точно адаптировать свои товары и услуги к предпочтениям потребителей, делая их более уникальными и персонализированными.\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_2_en.svg\" width=\"100%\"/>\n",
    "</center>\n",
    "\n",
    "<hr>"
   ]
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  {
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   "id": "f5d4b7ce-ce69-43b9-9145-c785e9e62091",
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    "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`"
   ]
  },
  {
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   "execution_count": 2,
   "id": "adb1d9a8-e7e4-4f30-ad1b-272ddc35533a",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<span style=\"color:#666\">**[</span><span style=\"color:#1776D2\">2023-12-16 19:05:15</span><span style=\"color:#666\">]</span> <span style=\"color:#666\">Извлечение признаков (экспертных и нейросетевых) из текста ...</span>** "
      ],
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       "<span style=\"color:#666\">**[</span><span style=\"color:#1776D2\">2023-12-16 19:05:17</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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       "  <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",
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       "    <tr>\n",
       "      <th>Person ID</th>\n",
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       "      <th>1</th>\n",
       "      <td>2d6btbaNdfo.000.mp4</td>\n",
       "      <td>0.581159</td>\n",
       "      <td>0.628822</td>\n",
       "      <td>0.466609</td>\n",
       "      <td>0.622129</td>\n",
       "      <td>0.553832</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>300gK3CnzW0.001.mp4</td>\n",
       "      <td>0.463991</td>\n",
       "      <td>0.418851</td>\n",
       "      <td>0.41301</td>\n",
       "      <td>0.493329</td>\n",
       "      <td>0.423093</td>\n",
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       "      <th>3</th>\n",
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       "      <td>0.454281</td>\n",
       "      <td>0.415049</td>\n",
       "      <td>0.39189</td>\n",
       "      <td>0.485114</td>\n",
       "      <td>0.420741</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4vdJGgZpj4k.003.mp4</td>\n",
       "      <td>0.588461</td>\n",
       "      <td>0.643233</td>\n",
       "      <td>0.530789</td>\n",
       "      <td>0.603038</td>\n",
       "      <td>0.593398</td>\n",
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       "      <th>5</th>\n",
       "      <td>be0DQawtVkE.002.mp4</td>\n",
       "      <td>0.633433</td>\n",
       "      <td>0.533295</td>\n",
       "      <td>0.523742</td>\n",
       "      <td>0.608591</td>\n",
       "      <td>0.588456</td>\n",
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       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>cLaZxEf1nE4.004.mp4</td>\n",
       "      <td>0.636944</td>\n",
       "      <td>0.542386</td>\n",
       "      <td>0.558461</td>\n",
       "      <td>0.570975</td>\n",
       "      <td>0.558983</td>\n",
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       "      <th>7</th>\n",
       "      <td>g24JGYuT74A.004.mp4</td>\n",
       "      <td>0.531518</td>\n",
       "      <td>0.376987</td>\n",
       "      <td>0.393309</td>\n",
       "      <td>0.4904</td>\n",
       "      <td>0.447881</td>\n",
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       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>JZNMxa3OKHY.000.mp4</td>\n",
       "      <td>0.610342</td>\n",
       "      <td>0.541418</td>\n",
       "      <td>0.563163</td>\n",
       "      <td>0.595013</td>\n",
       "      <td>0.569461</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>nvlqJbHk_Lc.003.mp4</td>\n",
       "      <td>0.495809</td>\n",
       "      <td>0.458526</td>\n",
       "      <td>0.414436</td>\n",
       "      <td>0.469152</td>\n",
       "      <td>0.435461</td>\n",
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       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>_plk5k7PBEg.003.mp4</td>\n",
       "      <td>0.60707</td>\n",
       "      <td>0.591893</td>\n",
       "      <td>0.520662</td>\n",
       "      <td>0.603938</td>\n",
       "      <td>0.565726</td>\n",
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       "                          Path  Openness Conscientiousness Extraversion  \\\n",
       "Person ID                                                                 \n",
       "1          2d6btbaNdfo.000.mp4  0.581159          0.628822     0.466609   \n",
       "2          300gK3CnzW0.001.mp4  0.463991          0.418851      0.41301   \n",
       "3          300gK3CnzW0.003.mp4  0.454281          0.415049      0.39189   \n",
       "4          4vdJGgZpj4k.003.mp4  0.588461          0.643233     0.530789   \n",
       "5          be0DQawtVkE.002.mp4  0.633433          0.533295     0.523742   \n",
       "6          cLaZxEf1nE4.004.mp4  0.636944          0.542386     0.558461   \n",
       "7          g24JGYuT74A.004.mp4  0.531518          0.376987     0.393309   \n",
       "8          JZNMxa3OKHY.000.mp4  0.610342          0.541418     0.563163   \n",
       "9          nvlqJbHk_Lc.003.mp4  0.495809          0.458526     0.414436   \n",
       "10         _plk5k7PBEg.003.mp4   0.60707          0.591893     0.520662   \n",
       "\n",
       "          Agreeableness Non-Neuroticism  \n",
       "Person ID                                \n",
       "1              0.622129        0.553832  \n",
       "2              0.493329        0.423093  \n",
       "3              0.485114        0.420741  \n",
       "4              0.603038        0.593398  \n",
       "5              0.608591        0.588456  \n",
       "6              0.570975        0.558983  \n",
       "7                0.4904        0.447881  \n",
       "8              0.595013        0.569461  \n",
       "9              0.469152        0.435461  \n",
       "10             0.603938        0.565726  "
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      "text/markdown": [
       "<span style=\"color:#666\">**[</span><span style=\"color:#1776D2\">2023-12-16 19:05:17</span><span style=\"color:#666\">]</span> <span style=\"color:#666\">Точность по отдельным персональным качествам личности человека ...</span>** "
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Openness</th>\n",
       "      <th>Conscientiousness</th>\n",
       "      <th>Extraversion</th>\n",
       "      <th>Agreeableness</th>\n",
       "      <th>Non-Neuroticism</th>\n",
       "      <th>Mean</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Metrics</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>MAE</th>\n",
       "      <td>0.0589</td>\n",
       "      <td>0.0612</td>\n",
       "      <td>0.0864</td>\n",
       "      <td>0.0697</td>\n",
       "      <td>0.0582</td>\n",
       "      <td>0.0669</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Accuracy</th>\n",
       "      <td>0.9411</td>\n",
       "      <td>0.9388</td>\n",
       "      <td>0.9136</td>\n",
       "      <td>0.9303</td>\n",
       "      <td>0.9418</td>\n",
       "      <td>0.9331</td>\n",
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       "          Openness  Conscientiousness  Extraversion  Agreeableness  \\\n",
       "Metrics                                                              \n",
       "MAE         0.0589             0.0612        0.0864         0.0697   \n",
       "Accuracy    0.9411             0.9388        0.9136         0.9303   \n",
       "\n",
       "          Non-Neuroticism    Mean  \n",
       "Metrics                            \n",
       "MAE                0.0582  0.0669  \n",
       "Accuracy           0.9418  0.9331  "
      ]
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       "<span style=\"color:#666\">**[</span><span style=\"color:#1776D2\">2023-12-16 19:05:17</span><span style=\"color:#666\">]</span> <span style=\"color:#666\">Средняя средних абсолютных ошибок: <span style=\"color:#1776D2\">0.0669</span>, средняя точность: <span style=\"color:#1776D2\">0.9331</span> ...</span>** "
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    {
     "data": {
      "text/markdown": [
       "<span style=\"color:#008001\">**Лог файлы успешно сохранены ...**</span>"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
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    },
    {
     "data": {
      "text/markdown": [
       "<span style=\"color:#666\">**--- Время выполнения: 64.147 сек. ---**</span>"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
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      "text/plain": [
       "True"
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   "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']['sberdisk']\n",
    "res_load_model_weights_hc = _b5.load_audio_model_weights_hc(url = url)\n",
    "\n",
    "url = _b5.weights_for_big5_['audio'][corpus]['nn']['sberdisk']\n",
    "res_load_model_weights_nn = _b5.load_audio_model_weights_nn(url = url)\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']['sberdisk']\n",
    "res_load_model_weights_hc = _b5.load_video_model_weights_hc(url = url)\n",
    "\n",
    "url = _b5.weights_for_big5_['video'][corpus]['fe']['sberdisk']\n",
    "res_load_model_weights_deep_fe = _b5.load_video_model_weights_deep_fe(url = url)\n",
    "\n",
    "url = _b5.weights_for_big5_['video'][corpus]['nn']['sberdisk']\n",
    "res_load_model_weights_nn = _b5.load_video_model_weights_nn(url = url)\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']['sberdisk']\n",
    "res_load_text_model_weights_hc_fi = _b5.load_text_model_weights_hc(url = url)\n",
    "\n",
    "url = _b5.weights_for_big5_['text'][corpus]['nn']['sberdisk']\n",
    "res_load_text_model_weights_nn_fi = _b5.load_text_model_weights_nn(url = url)\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']['sberdisk']\n",
    "res_load_avt_model_weights_b5 = _b5.load_avt_model_weights_b5(url = url)\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]['sberdisk']\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 прогнозирования потребительских предпочтений в промышленных товарах необходимо знать коэффициенты корреляции, определяющие взаимосвязь между персональными качествами личности человека и предпочтениями в товарах или услугах.\n",
    "\n",
    "В качестве примера предлагается использование коэффициентов корреляции между персональными качествами человека и характеристиками автомобилей, представленными в статье:\n",
    "\n",
    "1) O'Connor P. J. et al. What Drives Consumer Automobile Choice? Investigating Personality Trait Predictors of Vehicle Preference Factors // Personality and Individual Differences. – 2022. – Vol. 184. – pp. 111220.\n",
    "\n",
    "Пользователь может установить свои коэффициенты корреляции."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "13a9a3e6-31ad-414b-ac6c-1a4e30764d05",
   "metadata": {},
   "source": [
    "#### Прогнозирование потребительских предпочтений на характеристики атомобиля"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "e8d85c2a-1825-4f13-8ac7-a9033aa9a69a",
   "metadata": {},
   "outputs": [
    {
     "data": {
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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>Trait</th>\n",
       "      <th>Performance</th>\n",
       "      <th>Classic car features</th>\n",
       "      <th>Luxury additions</th>\n",
       "      <th>Fashion and attention</th>\n",
       "      <th>Recreation</th>\n",
       "      <th>Technology</th>\n",
       "      <th>Family friendly</th>\n",
       "      <th>Safe and reliable</th>\n",
       "      <th>Practical and easy to use</th>\n",
       "      <th>Economical/low cost</th>\n",
       "      <th>Basic features</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",
       "      <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>0.020000</td>\n",
       "      <td>-0.033333</td>\n",
       "      <td>-0.030000</td>\n",
       "      <td>-0.050000</td>\n",
       "      <td>0.033333</td>\n",
       "      <td>0.013333</td>\n",
       "      <td>-0.030000</td>\n",
       "      <td>0.136667</td>\n",
       "      <td>0.106667</td>\n",
       "      <td>0.093333</td>\n",
       "      <td>0.006667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Conscientiousness</td>\n",
       "      <td>0.013333</td>\n",
       "      <td>-0.193333</td>\n",
       "      <td>-0.063333</td>\n",
       "      <td>-0.096667</td>\n",
       "      <td>-0.096667</td>\n",
       "      <td>0.086667</td>\n",
       "      <td>-0.063333</td>\n",
       "      <td>0.280000</td>\n",
       "      <td>0.180000</td>\n",
       "      <td>0.130000</td>\n",
       "      <td>0.143333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Extraversion</td>\n",
       "      <td>0.133333</td>\n",
       "      <td>0.060000</td>\n",
       "      <td>0.106667</td>\n",
       "      <td>0.123333</td>\n",
       "      <td>0.126667</td>\n",
       "      <td>0.120000</td>\n",
       "      <td>0.090000</td>\n",
       "      <td>0.136667</td>\n",
       "      <td>0.043333</td>\n",
       "      <td>0.073333</td>\n",
       "      <td>0.050000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Agreeableness</td>\n",
       "      <td>-0.036667</td>\n",
       "      <td>-0.193333</td>\n",
       "      <td>-0.133333</td>\n",
       "      <td>-0.133333</td>\n",
       "      <td>-0.090000</td>\n",
       "      <td>0.046667</td>\n",
       "      <td>-0.016667</td>\n",
       "      <td>0.240000</td>\n",
       "      <td>0.160000</td>\n",
       "      <td>0.120000</td>\n",
       "      <td>0.083333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Non-Neuroticism</td>\n",
       "      <td>0.016667</td>\n",
       "      <td>-0.006667</td>\n",
       "      <td>-0.010000</td>\n",
       "      <td>-0.006667</td>\n",
       "      <td>-0.033333</td>\n",
       "      <td>0.046667</td>\n",
       "      <td>-0.023333</td>\n",
       "      <td>0.093333</td>\n",
       "      <td>0.046667</td>\n",
       "      <td>0.046667</td>\n",
       "      <td>-0.040000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                Trait  Performance  Classic car features  Luxury additions  \\\n",
       "ID                                                                           \n",
       "1            Openness     0.020000             -0.033333         -0.030000   \n",
       "2   Conscientiousness     0.013333             -0.193333         -0.063333   \n",
       "3        Extraversion     0.133333              0.060000          0.106667   \n",
       "4       Agreeableness    -0.036667             -0.193333         -0.133333   \n",
       "5     Non-Neuroticism     0.016667             -0.006667         -0.010000   \n",
       "\n",
       "    Fashion and attention  Recreation  Technology  Family friendly  \\\n",
       "ID                                                                   \n",
       "1               -0.050000    0.033333    0.013333        -0.030000   \n",
       "2               -0.096667   -0.096667    0.086667        -0.063333   \n",
       "3                0.123333    0.126667    0.120000         0.090000   \n",
       "4               -0.133333   -0.090000    0.046667        -0.016667   \n",
       "5               -0.006667   -0.033333    0.046667        -0.023333   \n",
       "\n",
       "    Safe and reliable  Practical and easy to use  Economical/low cost  \\\n",
       "ID                                                                      \n",
       "1            0.136667                   0.106667             0.093333   \n",
       "2            0.280000                   0.180000             0.130000   \n",
       "3            0.136667                   0.043333             0.073333   \n",
       "4            0.240000                   0.160000             0.120000   \n",
       "5            0.093333                   0.046667             0.046667   \n",
       "\n",
       "    Basic features  \n",
       "ID                  \n",
       "1         0.006667  \n",
       "2         0.143333  \n",
       "3         0.050000  \n",
       "4         0.083333  \n",
       "5        -0.040000  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Загрузка датафрейма с коэффициентами корреляции\n",
    "url = 'https://download.sberdisk.ru/download/file/478675818?token=EjfLMqOeK8cfnOu&filename=auto_characteristics.csv'\n",
    "df_correlation_coefficients = pd.read_csv(url)\n",
    "df_correlation_coefficients = pd.DataFrame(\n",
    "    df_correlation_coefficients.drop(['Style and performance', 'Safety and practicality'], axis = 1)\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": 4,
   "id": "c35d30c6-1042-4bd3-abbc-48081e44fd06",
   "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>Priority 1</th>\n",
       "      <th>Priority 2</th>\n",
       "      <th>Priority 3</th>\n",
       "      <th>Trait importance 1</th>\n",
       "      <th>Trait importance 2</th>\n",
       "      <th>Trait importance 3</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Person ID</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <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>0.581</td>\n",
       "      <td>0.629</td>\n",
       "      <td>0.467</td>\n",
       "      <td>0.622</td>\n",
       "      <td>0.554</td>\n",
       "      <td>Safe and reliable</td>\n",
       "      <td>Practical and easy to use</td>\n",
       "      <td>Economical/low cost</td>\n",
       "      <td>Conscientiousness</td>\n",
       "      <td>Agreeableness</td>\n",
       "      <td>Openness</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>300gK3CnzW0.001.mp4</td>\n",
       "      <td>0.464</td>\n",
       "      <td>0.419</td>\n",
       "      <td>0.413</td>\n",
       "      <td>0.493</td>\n",
       "      <td>0.423</td>\n",
       "      <td>Classic car features</td>\n",
       "      <td>Fashion and attention</td>\n",
       "      <td>Luxury additions</td>\n",
       "      <td>Agreeableness</td>\n",
       "      <td>Conscientiousness</td>\n",
       "      <td>Openness</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>300gK3CnzW0.003.mp4</td>\n",
       "      <td>0.454</td>\n",
       "      <td>0.415</td>\n",
       "      <td>0.392</td>\n",
       "      <td>0.485</td>\n",
       "      <td>0.421</td>\n",
       "      <td>Classic car features</td>\n",
       "      <td>Fashion and attention</td>\n",
       "      <td>Luxury additions</td>\n",
       "      <td>Agreeableness</td>\n",
       "      <td>Conscientiousness</td>\n",
       "      <td>Openness</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4vdJGgZpj4k.003.mp4</td>\n",
       "      <td>0.588</td>\n",
       "      <td>0.643</td>\n",
       "      <td>0.531</td>\n",
       "      <td>0.603</td>\n",
       "      <td>0.593</td>\n",
       "      <td>Safe and reliable</td>\n",
       "      <td>Practical and easy to use</td>\n",
       "      <td>Economical/low cost</td>\n",
       "      <td>Conscientiousness</td>\n",
       "      <td>Agreeableness</td>\n",
       "      <td>Openness</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>be0DQawtVkE.002.mp4</td>\n",
       "      <td>0.633</td>\n",
       "      <td>0.533</td>\n",
       "      <td>0.524</td>\n",
       "      <td>0.609</td>\n",
       "      <td>0.588</td>\n",
       "      <td>Practical and easy to use</td>\n",
       "      <td>Safe and reliable</td>\n",
       "      <td>Economical/low cost</td>\n",
       "      <td>Agreeableness</td>\n",
       "      <td>Openness</td>\n",
       "      <td>Non-Neuroticism</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>cLaZxEf1nE4.004.mp4</td>\n",
       "      <td>0.637</td>\n",
       "      <td>0.542</td>\n",
       "      <td>0.558</td>\n",
       "      <td>0.571</td>\n",
       "      <td>0.559</td>\n",
       "      <td>Safe and reliable</td>\n",
       "      <td>Economical/low cost</td>\n",
       "      <td>Practical and easy to use</td>\n",
       "      <td>Agreeableness</td>\n",
       "      <td>Openness</td>\n",
       "      <td>Extraversion</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>g24JGYuT74A.004.mp4</td>\n",
       "      <td>0.532</td>\n",
       "      <td>0.377</td>\n",
       "      <td>0.393</td>\n",
       "      <td>0.490</td>\n",
       "      <td>0.448</td>\n",
       "      <td>Classic car features</td>\n",
       "      <td>Fashion and attention</td>\n",
       "      <td>Luxury additions</td>\n",
       "      <td>Agreeableness</td>\n",
       "      <td>Conscientiousness</td>\n",
       "      <td>Openness</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>JZNMxa3OKHY.000.mp4</td>\n",
       "      <td>0.610</td>\n",
       "      <td>0.541</td>\n",
       "      <td>0.563</td>\n",
       "      <td>0.595</td>\n",
       "      <td>0.569</td>\n",
       "      <td>Safe and reliable</td>\n",
       "      <td>Economical/low cost</td>\n",
       "      <td>Practical and easy to use</td>\n",
       "      <td>Agreeableness</td>\n",
       "      <td>Openness</td>\n",
       "      <td>Extraversion</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>nvlqJbHk_Lc.003.mp4</td>\n",
       "      <td>0.496</td>\n",
       "      <td>0.459</td>\n",
       "      <td>0.414</td>\n",
       "      <td>0.469</td>\n",
       "      <td>0.435</td>\n",
       "      <td>Classic car features</td>\n",
       "      <td>Fashion and attention</td>\n",
       "      <td>Luxury additions</td>\n",
       "      <td>Agreeableness</td>\n",
       "      <td>Conscientiousness</td>\n",
       "      <td>Openness</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>_plk5k7PBEg.003.mp4</td>\n",
       "      <td>0.607</td>\n",
       "      <td>0.592</td>\n",
       "      <td>0.521</td>\n",
       "      <td>0.604</td>\n",
       "      <td>0.566</td>\n",
       "      <td>Safe and reliable</td>\n",
       "      <td>Practical and easy to use</td>\n",
       "      <td>Economical/low cost</td>\n",
       "      <td>Conscientiousness</td>\n",
       "      <td>Agreeableness</td>\n",
       "      <td>Openness</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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       "</div>"
      ],
      "text/plain": [
       "                          Path    OPE    CON    EXT    AGR   NNEU  \\\n",
       "Person ID                                                           \n",
       "1          2d6btbaNdfo.000.mp4  0.581  0.629  0.467  0.622  0.554   \n",
       "2          300gK3CnzW0.001.mp4  0.464  0.419  0.413  0.493  0.423   \n",
       "3          300gK3CnzW0.003.mp4  0.454  0.415  0.392  0.485  0.421   \n",
       "4          4vdJGgZpj4k.003.mp4  0.588  0.643  0.531  0.603  0.593   \n",
       "5          be0DQawtVkE.002.mp4  0.633  0.533  0.524  0.609  0.588   \n",
       "6          cLaZxEf1nE4.004.mp4  0.637  0.542  0.558  0.571  0.559   \n",
       "7          g24JGYuT74A.004.mp4  0.532  0.377  0.393  0.490  0.448   \n",
       "8          JZNMxa3OKHY.000.mp4  0.610  0.541  0.563  0.595  0.569   \n",
       "9          nvlqJbHk_Lc.003.mp4  0.496  0.459  0.414  0.469  0.435   \n",
       "10         _plk5k7PBEg.003.mp4  0.607  0.592  0.521  0.604  0.566   \n",
       "\n",
       "                          Priority 1                 Priority 2  \\\n",
       "Person ID                                                         \n",
       "1                  Safe and reliable  Practical and easy to use   \n",
       "2               Classic car features      Fashion and attention   \n",
       "3               Classic car features      Fashion and attention   \n",
       "4                  Safe and reliable  Practical and easy to use   \n",
       "5          Practical and easy to use          Safe and reliable   \n",
       "6                  Safe and reliable        Economical/low cost   \n",
       "7               Classic car features      Fashion and attention   \n",
       "8                  Safe and reliable        Economical/low cost   \n",
       "9               Classic car features      Fashion and attention   \n",
       "10                 Safe and reliable  Practical and easy to use   \n",
       "\n",
       "                          Priority 3 Trait importance 1 Trait importance 2  \\\n",
       "Person ID                                                                    \n",
       "1                Economical/low cost  Conscientiousness      Agreeableness   \n",
       "2                   Luxury additions      Agreeableness  Conscientiousness   \n",
       "3                   Luxury additions      Agreeableness  Conscientiousness   \n",
       "4                Economical/low cost  Conscientiousness      Agreeableness   \n",
       "5                Economical/low cost      Agreeableness           Openness   \n",
       "6          Practical and easy to use      Agreeableness           Openness   \n",
       "7                   Luxury additions      Agreeableness  Conscientiousness   \n",
       "8          Practical and easy to use      Agreeableness           Openness   \n",
       "9                   Luxury additions      Agreeableness  Conscientiousness   \n",
       "10               Economical/low cost  Conscientiousness      Agreeableness   \n",
       "\n",
       "          Trait importance 3  \n",
       "Person ID                     \n",
       "1                   Openness  \n",
       "2                   Openness  \n",
       "3                   Openness  \n",
       "4                   Openness  \n",
       "5            Non-Neuroticism  \n",
       "6               Extraversion  \n",
       "7                   Openness  \n",
       "8               Extraversion  \n",
       "9                   Openness  \n",
       "10                  Openness  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "_b5._priority_calculation(\n",
    "    correlation_coefficients = df_correlation_coefficients,\n",
    "    col_name_ocean = 'Trait',\n",
    "    threshold = 0.55,\n",
    "    number_priority = 3,\n",
    "    number_importance_traits = 3,\n",
    "    out = False\n",
    ")\n",
    "\n",
    "_b5._save_logs(df = _b5.df_files_priority_, name = 'auto_characteristics_priorities_fi_en', out = True)\n",
    "\n",
    "# Опционно\n",
    "df = _b5.df_files_priority_.rename(columns = {'Openness':'OPE', 'Conscientiousness':'CON', 'Extraversion': 'EXT', 'Agreeableness': 'AGR', 'Non-Neuroticism': 'NNEU'})\n",
    "columns_to_round = ['OPE', 'CON', 'EXT', 'AGR', 'NNEU']\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": "2101ce2f-aa33-4ff6-a040-ace532eae864",
   "metadata": {},
   "source": [
    "#### Прогнозирование потребительских предпочтений на характеристики мобильного устройства\n",
    "\n",
    "В качестве примера предлагается использование коэффициентов корреляции между персональными качествами человека и характеристиками мобильного устройства, представленными в статье:\n",
    "\n",
    "1) Peltonen E., Sharmila P., Asare K. O., Visuri A., Lagerspetz E., Ferreira D. (2020). When phones get personal: Predicting Big Five personality traits from application usage // Pervasive and Mobile Computing. – 2020. – Vol. 69. – 101269."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "4ed78d61-f5c7-4437-8e26-414fd2b3c9a1",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<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>Trait</th>\n",
       "      <th>Communication</th>\n",
       "      <th>Game Action</th>\n",
       "      <th>Game Board</th>\n",
       "      <th>Game Casino</th>\n",
       "      <th>Game Educational</th>\n",
       "      <th>Game Simulation</th>\n",
       "      <th>Game Trivia</th>\n",
       "      <th>Entertainment</th>\n",
       "      <th>Finance</th>\n",
       "      <th>Health and Fitness</th>\n",
       "      <th>Media and Video</th>\n",
       "      <th>Music and Audio</th>\n",
       "      <th>News and Magazines</th>\n",
       "      <th>Personalisation</th>\n",
       "      <th>Travel and Local</th>\n",
       "      <th>Weather</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",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Openness</td>\n",
       "      <td>0.118</td>\n",
       "      <td>0.056</td>\n",
       "      <td>0.079</td>\n",
       "      <td>0.342</td>\n",
       "      <td>0.027</td>\n",
       "      <td>0.104</td>\n",
       "      <td>0.026</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.006</td>\n",
       "      <td>0.002</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.001</td>\n",
       "      <td>0.004</td>\n",
       "      <td>0.002</td>\n",
       "      <td>0.004</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Conscientiousness</td>\n",
       "      <td>0.119</td>\n",
       "      <td>0.043</td>\n",
       "      <td>0.107</td>\n",
       "      <td>0.448</td>\n",
       "      <td>0.039</td>\n",
       "      <td>0.012</td>\n",
       "      <td>0.119</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.005</td>\n",
       "      <td>0.001</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.002</td>\n",
       "      <td>0.002</td>\n",
       "      <td>0.001</td>\n",
       "      <td>0.001</td>\n",
       "      <td>0.003</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Extraversion</td>\n",
       "      <td>0.246</td>\n",
       "      <td>0.182</td>\n",
       "      <td>0.211</td>\n",
       "      <td>0.311</td>\n",
       "      <td>0.102</td>\n",
       "      <td>0.165</td>\n",
       "      <td>0.223</td>\n",
       "      <td>0.001</td>\n",
       "      <td>0.003</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.001</td>\n",
       "      <td>0.001</td>\n",
       "      <td>0.001</td>\n",
       "      <td>0.004</td>\n",
       "      <td>0.009</td>\n",
       "      <td>0.003</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Agreeableness</td>\n",
       "      <td>0.218</td>\n",
       "      <td>0.104</td>\n",
       "      <td>0.164</td>\n",
       "      <td>0.284</td>\n",
       "      <td>0.165</td>\n",
       "      <td>0.122</td>\n",
       "      <td>0.162</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.003</td>\n",
       "      <td>0.001</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.002</td>\n",
       "      <td>0.002</td>\n",
       "      <td>0.001</td>\n",
       "      <td>0.004</td>\n",
       "      <td>0.003</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Non-Neuroticism</td>\n",
       "      <td>0.046</td>\n",
       "      <td>0.047</td>\n",
       "      <td>0.125</td>\n",
       "      <td>0.515</td>\n",
       "      <td>0.272</td>\n",
       "      <td>0.179</td>\n",
       "      <td>0.214</td>\n",
       "      <td>0.002</td>\n",
       "      <td>0.030</td>\n",
       "      <td>0.001</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.005</td>\n",
       "      <td>0.003</td>\n",
       "      <td>0.008</td>\n",
       "      <td>0.004</td>\n",
       "      <td>0.007</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                Trait  Communication  Game Action  Game Board  Game Casino  \\\n",
       "ID                                                                           \n",
       "1            Openness          0.118        0.056       0.079        0.342   \n",
       "2   Conscientiousness          0.119        0.043       0.107        0.448   \n",
       "3        Extraversion          0.246        0.182       0.211        0.311   \n",
       "4       Agreeableness          0.218        0.104       0.164        0.284   \n",
       "5     Non-Neuroticism          0.046        0.047       0.125        0.515   \n",
       "\n",
       "    Game Educational  Game Simulation  Game Trivia  Entertainment  Finance  \\\n",
       "ID                                                                           \n",
       "1              0.027            0.104        0.026          0.000    0.006   \n",
       "2              0.039            0.012        0.119          0.000    0.005   \n",
       "3              0.102            0.165        0.223          0.001    0.003   \n",
       "4              0.165            0.122        0.162          0.000    0.003   \n",
       "5              0.272            0.179        0.214          0.002    0.030   \n",
       "\n",
       "    Health and Fitness  Media and Video  Music and Audio  News and Magazines  \\\n",
       "ID                                                                             \n",
       "1                0.002            0.000            0.000               0.001   \n",
       "2                0.001            0.000            0.002               0.002   \n",
       "3                0.000            0.001            0.001               0.001   \n",
       "4                0.001            0.000            0.002               0.002   \n",
       "5                0.001            0.000            0.005               0.003   \n",
       "\n",
       "    Personalisation  Travel and Local  Weather  \n",
       "ID                                              \n",
       "1             0.004             0.002    0.004  \n",
       "2             0.001             0.001    0.003  \n",
       "3             0.004             0.009    0.003  \n",
       "4             0.001             0.004    0.003  \n",
       "5             0.008             0.004    0.007  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Загрузка датафрейма с коэффициентами корреляции\n",
    "url = 'https://download.sberdisk.ru/download/file/478676690?token=7KcAxPqMpWiYQnx&filename=divice_characteristics.csv'\n",
    "df_divice_characteristics = pd.read_csv(url)\n",
    "\n",
    "df_divice_characteristics.index.name = 'ID'\n",
    "df_divice_characteristics.index += 1\n",
    "df_divice_characteristics.index = df_divice_characteristics.index.map(str)\n",
    "\n",
    "df_divice_characteristics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "0807e4a4-f25f-4b1e-9a62-e381bba795d5",
   "metadata": {},
   "outputs": [
    {
     "data": {
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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>Priority 1</th>\n",
       "      <th>Priority 2</th>\n",
       "      <th>Priority 3</th>\n",
       "      <th>Trait importance 1</th>\n",
       "      <th>Trait importance 2</th>\n",
       "      <th>Trait importance 3</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Person ID</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <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>0.581</td>\n",
       "      <td>0.629</td>\n",
       "      <td>0.467</td>\n",
       "      <td>0.622</td>\n",
       "      <td>0.554</td>\n",
       "      <td>Game Casino</td>\n",
       "      <td>Game Educational</td>\n",
       "      <td>Game Trivia</td>\n",
       "      <td>Non-Neuroticism</td>\n",
       "      <td>Conscientiousness</td>\n",
       "      <td>Agreeableness</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>300gK3CnzW0.001.mp4</td>\n",
       "      <td>0.464</td>\n",
       "      <td>0.419</td>\n",
       "      <td>0.413</td>\n",
       "      <td>0.493</td>\n",
       "      <td>0.423</td>\n",
       "      <td>Media and Video</td>\n",
       "      <td>Entertainment</td>\n",
       "      <td>Health and Fitness</td>\n",
       "      <td>Conscientiousness</td>\n",
       "      <td>Agreeableness</td>\n",
       "      <td>Extraversion</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>300gK3CnzW0.003.mp4</td>\n",
       "      <td>0.454</td>\n",
       "      <td>0.415</td>\n",
       "      <td>0.392</td>\n",
       "      <td>0.485</td>\n",
       "      <td>0.421</td>\n",
       "      <td>Media and Video</td>\n",
       "      <td>Entertainment</td>\n",
       "      <td>Health and Fitness</td>\n",
       "      <td>Conscientiousness</td>\n",
       "      <td>Agreeableness</td>\n",
       "      <td>Extraversion</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4vdJGgZpj4k.003.mp4</td>\n",
       "      <td>0.588</td>\n",
       "      <td>0.643</td>\n",
       "      <td>0.531</td>\n",
       "      <td>0.603</td>\n",
       "      <td>0.593</td>\n",
       "      <td>Game Casino</td>\n",
       "      <td>Game Educational</td>\n",
       "      <td>Game Trivia</td>\n",
       "      <td>Non-Neuroticism</td>\n",
       "      <td>Conscientiousness</td>\n",
       "      <td>Agreeableness</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>be0DQawtVkE.002.mp4</td>\n",
       "      <td>0.633</td>\n",
       "      <td>0.533</td>\n",
       "      <td>0.524</td>\n",
       "      <td>0.609</td>\n",
       "      <td>0.588</td>\n",
       "      <td>Game Casino</td>\n",
       "      <td>Game Educational</td>\n",
       "      <td>Game Simulation</td>\n",
       "      <td>Non-Neuroticism</td>\n",
       "      <td>Agreeableness</td>\n",
       "      <td>Openness</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>cLaZxEf1nE4.004.mp4</td>\n",
       "      <td>0.637</td>\n",
       "      <td>0.542</td>\n",
       "      <td>0.558</td>\n",
       "      <td>0.571</td>\n",
       "      <td>0.559</td>\n",
       "      <td>Game Casino</td>\n",
       "      <td>Game Simulation</td>\n",
       "      <td>Game Educational</td>\n",
       "      <td>Non-Neuroticism</td>\n",
       "      <td>Agreeableness</td>\n",
       "      <td>Extraversion</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>g24JGYuT74A.004.mp4</td>\n",
       "      <td>0.532</td>\n",
       "      <td>0.377</td>\n",
       "      <td>0.393</td>\n",
       "      <td>0.490</td>\n",
       "      <td>0.448</td>\n",
       "      <td>Media and Video</td>\n",
       "      <td>Entertainment</td>\n",
       "      <td>Health and Fitness</td>\n",
       "      <td>Conscientiousness</td>\n",
       "      <td>Agreeableness</td>\n",
       "      <td>Extraversion</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>JZNMxa3OKHY.000.mp4</td>\n",
       "      <td>0.610</td>\n",
       "      <td>0.541</td>\n",
       "      <td>0.563</td>\n",
       "      <td>0.595</td>\n",
       "      <td>0.569</td>\n",
       "      <td>Game Casino</td>\n",
       "      <td>Game Simulation</td>\n",
       "      <td>Game Educational</td>\n",
       "      <td>Non-Neuroticism</td>\n",
       "      <td>Agreeableness</td>\n",
       "      <td>Extraversion</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>nvlqJbHk_Lc.003.mp4</td>\n",
       "      <td>0.496</td>\n",
       "      <td>0.459</td>\n",
       "      <td>0.414</td>\n",
       "      <td>0.469</td>\n",
       "      <td>0.435</td>\n",
       "      <td>Media and Video</td>\n",
       "      <td>Entertainment</td>\n",
       "      <td>Health and Fitness</td>\n",
       "      <td>Conscientiousness</td>\n",
       "      <td>Agreeableness</td>\n",
       "      <td>Extraversion</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>_plk5k7PBEg.003.mp4</td>\n",
       "      <td>0.607</td>\n",
       "      <td>0.592</td>\n",
       "      <td>0.521</td>\n",
       "      <td>0.604</td>\n",
       "      <td>0.566</td>\n",
       "      <td>Game Casino</td>\n",
       "      <td>Game Educational</td>\n",
       "      <td>Game Trivia</td>\n",
       "      <td>Non-Neuroticism</td>\n",
       "      <td>Agreeableness</td>\n",
       "      <td>Conscientiousness</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  0.581  0.629  0.467  0.622  0.554   \n",
       "2          300gK3CnzW0.001.mp4  0.464  0.419  0.413  0.493  0.423   \n",
       "3          300gK3CnzW0.003.mp4  0.454  0.415  0.392  0.485  0.421   \n",
       "4          4vdJGgZpj4k.003.mp4  0.588  0.643  0.531  0.603  0.593   \n",
       "5          be0DQawtVkE.002.mp4  0.633  0.533  0.524  0.609  0.588   \n",
       "6          cLaZxEf1nE4.004.mp4  0.637  0.542  0.558  0.571  0.559   \n",
       "7          g24JGYuT74A.004.mp4  0.532  0.377  0.393  0.490  0.448   \n",
       "8          JZNMxa3OKHY.000.mp4  0.610  0.541  0.563  0.595  0.569   \n",
       "9          nvlqJbHk_Lc.003.mp4  0.496  0.459  0.414  0.469  0.435   \n",
       "10         _plk5k7PBEg.003.mp4  0.607  0.592  0.521  0.604  0.566   \n",
       "\n",
       "                Priority 1        Priority 2          Priority 3  \\\n",
       "Person ID                                                          \n",
       "1              Game Casino  Game Educational         Game Trivia   \n",
       "2          Media and Video     Entertainment  Health and Fitness   \n",
       "3          Media and Video     Entertainment  Health and Fitness   \n",
       "4              Game Casino  Game Educational         Game Trivia   \n",
       "5              Game Casino  Game Educational     Game Simulation   \n",
       "6              Game Casino   Game Simulation    Game Educational   \n",
       "7          Media and Video     Entertainment  Health and Fitness   \n",
       "8              Game Casino   Game Simulation    Game Educational   \n",
       "9          Media and Video     Entertainment  Health and Fitness   \n",
       "10             Game Casino  Game Educational         Game Trivia   \n",
       "\n",
       "          Trait importance 1 Trait importance 2 Trait importance 3  \n",
       "Person ID                                                           \n",
       "1            Non-Neuroticism  Conscientiousness      Agreeableness  \n",
       "2          Conscientiousness      Agreeableness       Extraversion  \n",
       "3          Conscientiousness      Agreeableness       Extraversion  \n",
       "4            Non-Neuroticism  Conscientiousness      Agreeableness  \n",
       "5            Non-Neuroticism      Agreeableness           Openness  \n",
       "6            Non-Neuroticism      Agreeableness       Extraversion  \n",
       "7          Conscientiousness      Agreeableness       Extraversion  \n",
       "8            Non-Neuroticism      Agreeableness       Extraversion  \n",
       "9          Conscientiousness      Agreeableness       Extraversion  \n",
       "10           Non-Neuroticism      Agreeableness  Conscientiousness  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "_b5._priority_calculation(\n",
    "    correlation_coefficients = df_divice_characteristics,\n",
    "    col_name_ocean = 'Trait',\n",
    "    threshold = 0.55,\n",
    "    number_priority = 3,\n",
    "    number_importance_traits = 3,\n",
    "    out = True\n",
    ")\n",
    "\n",
    "_b5._save_logs(df = _b5.df_files_priority_, name = 'divice_characteristics_priorities_fi_en', out = True)\n",
    "\n",
    "# Опционно\n",
    "df = _b5.df_files_priority_.rename(columns = {'Openness':'OPE', 'Conscientiousness':'CON', 'Extraversion': 'EXT', 'Agreeableness': 'AGR', 'Non-Neuroticism': 'NNEU'})\n",
    "columns_to_round = ['OPE', 'CON', 'EXT', 'AGR', 'NNEU']\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": 7,
   "id": "3887d07c-eef2-4980-8d82-cabf6568aa7d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/markdown": [
       "<span style=\"color:#666\">**[</span><span style=\"color:#1776D2\">2023-12-16 19:13:25</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\">2023-12-16 19:13:30</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>"
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     },
     "metadata": {},
     "output_type": "display_data"
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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>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>0.758137</td>\n",
       "      <td>0.693356</td>\n",
       "      <td>0.650108</td>\n",
       "      <td>0.744589</td>\n",
       "      <td>0.488671</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>speaker_06_center_83.mov</td>\n",
       "      <td>0.681602</td>\n",
       "      <td>0.654339</td>\n",
       "      <td>0.607156</td>\n",
       "      <td>0.731282</td>\n",
       "      <td>0.417908</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>speaker_07_center_83.mov</td>\n",
       "      <td>0.666104</td>\n",
       "      <td>0.656836</td>\n",
       "      <td>0.567863</td>\n",
       "      <td>0.685067</td>\n",
       "      <td>0.378102</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>speaker_10_center_83.mov</td>\n",
       "      <td>0.694171</td>\n",
       "      <td>0.596195</td>\n",
       "      <td>0.571414</td>\n",
       "      <td>0.66223</td>\n",
       "      <td>0.348639</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>speaker_11_center_83.mov</td>\n",
       "      <td>0.712885</td>\n",
       "      <td>0.594764</td>\n",
       "      <td>0.571709</td>\n",
       "      <td>0.716696</td>\n",
       "      <td>0.37802</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>speaker_15_center_83.mov</td>\n",
       "      <td>0.664158</td>\n",
       "      <td>0.670411</td>\n",
       "      <td>0.60421</td>\n",
       "      <td>0.696056</td>\n",
       "      <td>0.399842</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>speaker_19_center_83.mov</td>\n",
       "      <td>0.761213</td>\n",
       "      <td>0.652635</td>\n",
       "      <td>0.651028</td>\n",
       "      <td>0.788677</td>\n",
       "      <td>0.459676</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>speaker_23_center_83.mov</td>\n",
       "      <td>0.692788</td>\n",
       "      <td>0.68324</td>\n",
       "      <td>0.616737</td>\n",
       "      <td>0.795205</td>\n",
       "      <td>0.447242</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>speaker_24_center_83.mov</td>\n",
       "      <td>0.705923</td>\n",
       "      <td>0.658382</td>\n",
       "      <td>0.610645</td>\n",
       "      <td>0.697415</td>\n",
       "      <td>0.411988</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>speaker_27_center_83.mov</td>\n",
       "      <td>0.753417</td>\n",
       "      <td>0.708372</td>\n",
       "      <td>0.654608</td>\n",
       "      <td>0.816416</td>\n",
       "      <td>0.504743</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  0.758137          0.693356     0.650108   \n",
       "2          speaker_06_center_83.mov  0.681602          0.654339     0.607156   \n",
       "3          speaker_07_center_83.mov  0.666104          0.656836     0.567863   \n",
       "4          speaker_10_center_83.mov  0.694171          0.596195     0.571414   \n",
       "5          speaker_11_center_83.mov  0.712885          0.594764     0.571709   \n",
       "6          speaker_15_center_83.mov  0.664158          0.670411      0.60421   \n",
       "7          speaker_19_center_83.mov  0.761213          0.652635     0.651028   \n",
       "8          speaker_23_center_83.mov  0.692788           0.68324     0.616737   \n",
       "9          speaker_24_center_83.mov  0.705923          0.658382     0.610645   \n",
       "10         speaker_27_center_83.mov  0.753417          0.708372     0.654608   \n",
       "\n",
       "          Agreeableness Non-Neuroticism  \n",
       "Person ID                                \n",
       "1              0.744589        0.488671  \n",
       "2              0.731282        0.417908  \n",
       "3              0.685067        0.378102  \n",
       "4               0.66223        0.348639  \n",
       "5              0.716696         0.37802  \n",
       "6              0.696056        0.399842  \n",
       "7              0.788677        0.459676  \n",
       "8              0.795205        0.447242  \n",
       "9              0.697415        0.411988  \n",
       "10             0.816416        0.504743  "
      ]
     },
     "metadata": {},
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    },
    {
     "data": {
      "text/markdown": [
       "<span style=\"color:#666\">**[</span><span style=\"color:#1776D2\">2023-12-16 19:13:30</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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       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Openness</th>\n",
       "      <th>Conscientiousness</th>\n",
       "      <th>Extraversion</th>\n",
       "      <th>Agreeableness</th>\n",
       "      <th>Non-Neuroticism</th>\n",
       "      <th>Mean</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Metrics</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>MAE</th>\n",
       "      <td>0.0673</td>\n",
       "      <td>0.0789</td>\n",
       "      <td>0.1325</td>\n",
       "      <td>0.102</td>\n",
       "      <td>0.1002</td>\n",
       "      <td>0.0962</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Accuracy</th>\n",
       "      <td>0.9327</td>\n",
       "      <td>0.9211</td>\n",
       "      <td>0.8675</td>\n",
       "      <td>0.898</td>\n",
       "      <td>0.8998</td>\n",
       "      <td>0.9038</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          Openness  Conscientiousness  Extraversion  Agreeableness  \\\n",
       "Metrics                                                              \n",
       "MAE         0.0673             0.0789        0.1325          0.102   \n",
       "Accuracy    0.9327             0.9211        0.8675          0.898   \n",
       "\n",
       "          Non-Neuroticism    Mean  \n",
       "Metrics                            \n",
       "MAE                0.1002  0.0962  \n",
       "Accuracy           0.8998  0.9038  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "<span style=\"color:#666\">**[</span><span style=\"color:#1776D2\">2023-12-16 19:13:30</span><span style=\"color:#666\">]</span> <span style=\"color:#666\">Средняя средних абсолютных ошибок: <span style=\"color:#1776D2\">0.0962</span>, средняя точность: <span style=\"color:#1776D2\">0.9038</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: 416.453 сек. ---**</span>"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 7,
     "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']['sberdisk']\n",
    "res_load_model_weights_hc = _b5.load_audio_model_weights_hc(url = url)\n",
    "\n",
    "url = _b5.weights_for_big5_['audio'][corpus]['nn']['sberdisk']\n",
    "res_load_model_weights_nn = _b5.load_audio_model_weights_nn(url = url)\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']['sberdisk']\n",
    "res_load_model_weights_hc = _b5.load_video_model_weights_hc(url = url)\n",
    "\n",
    "url = _b5.weights_for_big5_['video'][corpus]['fe']['sberdisk']\n",
    "res_load_model_weights_deep_fe = _b5.load_video_model_weights_deep_fe(url = url)\n",
    "\n",
    "url = _b5.weights_for_big5_['video'][corpus]['nn']['sberdisk']\n",
    "res_load_model_weights_nn = _b5.load_video_model_weights_nn(url = url)\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']['sberdisk']\n",
    "res_load_text_model_weights_hc_fi = _b5.load_text_model_weights_hc(url = url)\n",
    "\n",
    "url = _b5.weights_for_big5_['text'][corpus]['nn']['sberdisk']\n",
    "res_load_text_model_weights_nn_fi = _b5.load_text_model_weights_nn(url = url)\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']['sberdisk']\n",
    "res_load_avt_model_weights_b5 = _b5.load_avt_model_weights_b5(url = url)\n",
    "\n",
    "PATH_TO_DIR = './video_MuPTA/'\n",
    "PATH_SAVE_VIDEO = './video_MuPTA/test/'\n",
    "\n",
    "_b5.path_to_save_ = PATH_SAVE_VIDEO\n",
    "\n",
    "# Загрузка 10 тестовых аудиовидеозаписей из корпуса MuPTA\n",
    "# URL: https://hci.nw.ru/en/pages/mupta-corpus\n",
    "domain = 'https://download.sberdisk.ru/download/file/'\n",
    "tets_name_files = [\n",
    "    '477995979?token=2cvyk7CS0mHx2MJ&filename=speaker_06_center_83.mov',\n",
    "    '477995980?token=jGPtBPS69uzFU6Y&filename=speaker_01_center_83.mov',\n",
    "    '477995967?token=zCaRbNB6ht5wMPq&filename=speaker_11_center_83.mov',\n",
    "    '477995966?token=B1rbinDYRQKrI3T&filename=speaker_15_center_83.mov',\n",
    "    '477995978?token=dEpVDtZg1EQiEQ9&filename=speaker_07_center_83.mov',\n",
    "    '477995961?token=o1hVjw8G45q9L9Z&filename=speaker_19_center_83.mov',\n",
    "    '477995964?token=5K220Aqf673VHPq&filename=speaker_23_center_83.mov',\n",
    "    '477995965?token=v1LVD2KT1cU7Lpb&filename=speaker_24_center_83.mov',\n",
    "    '477995962?token=tmaSGyyWLA6XCy9&filename=speaker_27_center_83.mov',\n",
    "    '477995963?token=bTpo96qNDPcwGqb&filename=speaker_10_center_83.mov',\n",
    "]\n",
    "\n",
    "for curr_files in tets_name_files:\n",
    "    _b5.download_file_from_url(url = domain + curr_files, out = True)\n",
    "\n",
    "# Получение прогнозов\n",
    "_b5.path_to_dataset_ = PATH_TO_DIR # Директория набора данных\n",
    "_b5.ext_ = ['.mov'] # Расширения искомых файлов\n",
    "\n",
    "# Полный путь к файлу с верными предсказаниями для подсчета точности\n",
    "url_accuracy = _b5.true_traits_['mupta']['sberdisk']\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 прогнозирования потребительских предпочтений в промышленных товарах необходимо знать коэффициенты корреляции, определяющие взаимосвязь между персональными качествами личности человека и предпочтениями в товарах или услугах.\n",
    "\n",
    "В качестве примера предлагается использование коэффициентов корреляции между персональными качествами человека и характеристиками автомобилей, представленными в статье:\n",
    "\n",
    "1) O'Connor P. J. et al. What Drives Consumer Automobile Choice? Investigating Personality Trait Predictors of Vehicle Preference Factors // Personality and Individual Differences. – 2022. – Vol. 184. – pp. 111220.\n",
    "\n",
    "Пользователь может установить свои коэффициенты корреляции."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1ef5d32b-bb48-4e0d-8cce-9d0cff815e80",
   "metadata": {},
   "source": [
    "#### Прогнозирование потребительских предпочтений на характеристики атомобиля"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "f79ba8a2-c173-4d83-9cf7-d0c991c7bcc0",
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Trait</th>\n",
       "      <th>Performance</th>\n",
       "      <th>Classic car features</th>\n",
       "      <th>Luxury additions</th>\n",
       "      <th>Fashion and attention</th>\n",
       "      <th>Recreation</th>\n",
       "      <th>Technology</th>\n",
       "      <th>Family friendly</th>\n",
       "      <th>Safe and reliable</th>\n",
       "      <th>Practical and easy to use</th>\n",
       "      <th>Economical/low cost</th>\n",
       "      <th>Basic features</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",
       "      <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>0.020000</td>\n",
       "      <td>-0.033333</td>\n",
       "      <td>-0.030000</td>\n",
       "      <td>-0.050000</td>\n",
       "      <td>0.033333</td>\n",
       "      <td>0.013333</td>\n",
       "      <td>-0.030000</td>\n",
       "      <td>0.136667</td>\n",
       "      <td>0.106667</td>\n",
       "      <td>0.093333</td>\n",
       "      <td>0.006667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Conscientiousness</td>\n",
       "      <td>0.013333</td>\n",
       "      <td>-0.193333</td>\n",
       "      <td>-0.063333</td>\n",
       "      <td>-0.096667</td>\n",
       "      <td>-0.096667</td>\n",
       "      <td>0.086667</td>\n",
       "      <td>-0.063333</td>\n",
       "      <td>0.280000</td>\n",
       "      <td>0.180000</td>\n",
       "      <td>0.130000</td>\n",
       "      <td>0.143333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Extraversion</td>\n",
       "      <td>0.133333</td>\n",
       "      <td>0.060000</td>\n",
       "      <td>0.106667</td>\n",
       "      <td>0.123333</td>\n",
       "      <td>0.126667</td>\n",
       "      <td>0.120000</td>\n",
       "      <td>0.090000</td>\n",
       "      <td>0.136667</td>\n",
       "      <td>0.043333</td>\n",
       "      <td>0.073333</td>\n",
       "      <td>0.050000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Agreeableness</td>\n",
       "      <td>-0.036667</td>\n",
       "      <td>-0.193333</td>\n",
       "      <td>-0.133333</td>\n",
       "      <td>-0.133333</td>\n",
       "      <td>-0.090000</td>\n",
       "      <td>0.046667</td>\n",
       "      <td>-0.016667</td>\n",
       "      <td>0.240000</td>\n",
       "      <td>0.160000</td>\n",
       "      <td>0.120000</td>\n",
       "      <td>0.083333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Non-Neuroticism</td>\n",
       "      <td>0.016667</td>\n",
       "      <td>-0.006667</td>\n",
       "      <td>-0.010000</td>\n",
       "      <td>-0.006667</td>\n",
       "      <td>-0.033333</td>\n",
       "      <td>0.046667</td>\n",
       "      <td>-0.023333</td>\n",
       "      <td>0.093333</td>\n",
       "      <td>0.046667</td>\n",
       "      <td>0.046667</td>\n",
       "      <td>-0.040000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                Trait  Performance  Classic car features  Luxury additions  \\\n",
       "ID                                                                           \n",
       "1            Openness     0.020000             -0.033333         -0.030000   \n",
       "2   Conscientiousness     0.013333             -0.193333         -0.063333   \n",
       "3        Extraversion     0.133333              0.060000          0.106667   \n",
       "4       Agreeableness    -0.036667             -0.193333         -0.133333   \n",
       "5     Non-Neuroticism     0.016667             -0.006667         -0.010000   \n",
       "\n",
       "    Fashion and attention  Recreation  Technology  Family friendly  \\\n",
       "ID                                                                   \n",
       "1               -0.050000    0.033333    0.013333        -0.030000   \n",
       "2               -0.096667   -0.096667    0.086667        -0.063333   \n",
       "3                0.123333    0.126667    0.120000         0.090000   \n",
       "4               -0.133333   -0.090000    0.046667        -0.016667   \n",
       "5               -0.006667   -0.033333    0.046667        -0.023333   \n",
       "\n",
       "    Safe and reliable  Practical and easy to use  Economical/low cost  \\\n",
       "ID                                                                      \n",
       "1            0.136667                   0.106667             0.093333   \n",
       "2            0.280000                   0.180000             0.130000   \n",
       "3            0.136667                   0.043333             0.073333   \n",
       "4            0.240000                   0.160000             0.120000   \n",
       "5            0.093333                   0.046667             0.046667   \n",
       "\n",
       "    Basic features  \n",
       "ID                  \n",
       "1         0.006667  \n",
       "2         0.143333  \n",
       "3         0.050000  \n",
       "4         0.083333  \n",
       "5        -0.040000  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Загрузка датафрейма с коэффициентами корреляции\n",
    "url = 'https://download.sberdisk.ru/download/file/478675818?token=EjfLMqOeK8cfnOu&filename=auto_characteristics.csv'\n",
    "df_correlation_coefficients = pd.read_csv(url)\n",
    "df_correlation_coefficients = pd.DataFrame(\n",
    "    df_correlation_coefficients.drop(['Style and performance', 'Safety and practicality'], axis = 1)\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": "16e43257-3500-4dd8-b04a-34f7435fc185",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
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       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Path</th>\n",
       "      <th>OPE</th>\n",
       "      <th>CON</th>\n",
       "      <th>EXT</th>\n",
       "      <th>AGR</th>\n",
       "      <th>NNEU</th>\n",
       "      <th>Priority 1</th>\n",
       "      <th>Priority 2</th>\n",
       "      <th>Priority 3</th>\n",
       "      <th>Trait importance 1</th>\n",
       "      <th>Trait importance 2</th>\n",
       "      <th>Trait importance 3</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Person ID</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <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>0.758</td>\n",
       "      <td>0.693</td>\n",
       "      <td>0.650</td>\n",
       "      <td>0.745</td>\n",
       "      <td>0.489</td>\n",
       "      <td>Safe and reliable</td>\n",
       "      <td>Practical and easy to use</td>\n",
       "      <td>Economical/low cost</td>\n",
       "      <td>Conscientiousness</td>\n",
       "      <td>Agreeableness</td>\n",
       "      <td>Openness</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>speaker_06_center_83.mov</td>\n",
       "      <td>0.682</td>\n",
       "      <td>0.654</td>\n",
       "      <td>0.607</td>\n",
       "      <td>0.731</td>\n",
       "      <td>0.418</td>\n",
       "      <td>Safe and reliable</td>\n",
       "      <td>Practical and easy to use</td>\n",
       "      <td>Economical/low cost</td>\n",
       "      <td>Conscientiousness</td>\n",
       "      <td>Agreeableness</td>\n",
       "      <td>Openness</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>speaker_07_center_83.mov</td>\n",
       "      <td>0.666</td>\n",
       "      <td>0.657</td>\n",
       "      <td>0.568</td>\n",
       "      <td>0.685</td>\n",
       "      <td>0.378</td>\n",
       "      <td>Safe and reliable</td>\n",
       "      <td>Practical and easy to use</td>\n",
       "      <td>Economical/low cost</td>\n",
       "      <td>Conscientiousness</td>\n",
       "      <td>Agreeableness</td>\n",
       "      <td>Openness</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>speaker_10_center_83.mov</td>\n",
       "      <td>0.694</td>\n",
       "      <td>0.596</td>\n",
       "      <td>0.571</td>\n",
       "      <td>0.662</td>\n",
       "      <td>0.349</td>\n",
       "      <td>Safe and reliable</td>\n",
       "      <td>Practical and easy to use</td>\n",
       "      <td>Economical/low cost</td>\n",
       "      <td>Conscientiousness</td>\n",
       "      <td>Agreeableness</td>\n",
       "      <td>Openness</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>speaker_11_center_83.mov</td>\n",
       "      <td>0.713</td>\n",
       "      <td>0.595</td>\n",
       "      <td>0.572</td>\n",
       "      <td>0.717</td>\n",
       "      <td>0.378</td>\n",
       "      <td>Safe and reliable</td>\n",
       "      <td>Practical and easy to use</td>\n",
       "      <td>Economical/low cost</td>\n",
       "      <td>Agreeableness</td>\n",
       "      <td>Conscientiousness</td>\n",
       "      <td>Openness</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>speaker_15_center_83.mov</td>\n",
       "      <td>0.664</td>\n",
       "      <td>0.670</td>\n",
       "      <td>0.604</td>\n",
       "      <td>0.696</td>\n",
       "      <td>0.400</td>\n",
       "      <td>Safe and reliable</td>\n",
       "      <td>Practical and easy to use</td>\n",
       "      <td>Economical/low cost</td>\n",
       "      <td>Conscientiousness</td>\n",
       "      <td>Agreeableness</td>\n",
       "      <td>Openness</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>speaker_19_center_83.mov</td>\n",
       "      <td>0.761</td>\n",
       "      <td>0.653</td>\n",
       "      <td>0.651</td>\n",
       "      <td>0.789</td>\n",
       "      <td>0.460</td>\n",
       "      <td>Safe and reliable</td>\n",
       "      <td>Practical and easy to use</td>\n",
       "      <td>Economical/low cost</td>\n",
       "      <td>Agreeableness</td>\n",
       "      <td>Conscientiousness</td>\n",
       "      <td>Openness</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>speaker_23_center_83.mov</td>\n",
       "      <td>0.693</td>\n",
       "      <td>0.683</td>\n",
       "      <td>0.617</td>\n",
       "      <td>0.795</td>\n",
       "      <td>0.447</td>\n",
       "      <td>Safe and reliable</td>\n",
       "      <td>Practical and easy to use</td>\n",
       "      <td>Economical/low cost</td>\n",
       "      <td>Agreeableness</td>\n",
       "      <td>Conscientiousness</td>\n",
       "      <td>Openness</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>speaker_24_center_83.mov</td>\n",
       "      <td>0.706</td>\n",
       "      <td>0.658</td>\n",
       "      <td>0.611</td>\n",
       "      <td>0.697</td>\n",
       "      <td>0.412</td>\n",
       "      <td>Safe and reliable</td>\n",
       "      <td>Practical and easy to use</td>\n",
       "      <td>Economical/low cost</td>\n",
       "      <td>Conscientiousness</td>\n",
       "      <td>Agreeableness</td>\n",
       "      <td>Openness</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>speaker_27_center_83.mov</td>\n",
       "      <td>0.753</td>\n",
       "      <td>0.708</td>\n",
       "      <td>0.655</td>\n",
       "      <td>0.816</td>\n",
       "      <td>0.505</td>\n",
       "      <td>Safe and reliable</td>\n",
       "      <td>Practical and easy to use</td>\n",
       "      <td>Economical/low cost</td>\n",
       "      <td>Agreeableness</td>\n",
       "      <td>Conscientiousness</td>\n",
       "      <td>Openness</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                               Path    OPE    CON    EXT    AGR   NNEU  \\\n",
       "Person ID                                                                \n",
       "1          speaker_01_center_83.mov  0.758  0.693  0.650  0.745  0.489   \n",
       "2          speaker_06_center_83.mov  0.682  0.654  0.607  0.731  0.418   \n",
       "3          speaker_07_center_83.mov  0.666  0.657  0.568  0.685  0.378   \n",
       "4          speaker_10_center_83.mov  0.694  0.596  0.571  0.662  0.349   \n",
       "5          speaker_11_center_83.mov  0.713  0.595  0.572  0.717  0.378   \n",
       "6          speaker_15_center_83.mov  0.664  0.670  0.604  0.696  0.400   \n",
       "7          speaker_19_center_83.mov  0.761  0.653  0.651  0.789  0.460   \n",
       "8          speaker_23_center_83.mov  0.693  0.683  0.617  0.795  0.447   \n",
       "9          speaker_24_center_83.mov  0.706  0.658  0.611  0.697  0.412   \n",
       "10         speaker_27_center_83.mov  0.753  0.708  0.655  0.816  0.505   \n",
       "\n",
       "                  Priority 1                 Priority 2           Priority 3  \\\n",
       "Person ID                                                                      \n",
       "1          Safe and reliable  Practical and easy to use  Economical/low cost   \n",
       "2          Safe and reliable  Practical and easy to use  Economical/low cost   \n",
       "3          Safe and reliable  Practical and easy to use  Economical/low cost   \n",
       "4          Safe and reliable  Practical and easy to use  Economical/low cost   \n",
       "5          Safe and reliable  Practical and easy to use  Economical/low cost   \n",
       "6          Safe and reliable  Practical and easy to use  Economical/low cost   \n",
       "7          Safe and reliable  Practical and easy to use  Economical/low cost   \n",
       "8          Safe and reliable  Practical and easy to use  Economical/low cost   \n",
       "9          Safe and reliable  Practical and easy to use  Economical/low cost   \n",
       "10         Safe and reliable  Practical and easy to use  Economical/low cost   \n",
       "\n",
       "          Trait importance 1 Trait importance 2 Trait importance 3  \n",
       "Person ID                                                           \n",
       "1          Conscientiousness      Agreeableness           Openness  \n",
       "2          Conscientiousness      Agreeableness           Openness  \n",
       "3          Conscientiousness      Agreeableness           Openness  \n",
       "4          Conscientiousness      Agreeableness           Openness  \n",
       "5              Agreeableness  Conscientiousness           Openness  \n",
       "6          Conscientiousness      Agreeableness           Openness  \n",
       "7              Agreeableness  Conscientiousness           Openness  \n",
       "8              Agreeableness  Conscientiousness           Openness  \n",
       "9          Conscientiousness      Agreeableness           Openness  \n",
       "10             Agreeableness  Conscientiousness           Openness  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "_b5._priority_calculation(\n",
    "    correlation_coefficients = df_correlation_coefficients,\n",
    "    col_name_ocean = 'Trait',\n",
    "    threshold = 0.55,\n",
    "    number_priority = 3,\n",
    "    number_importance_traits = 3,\n",
    "    out = False\n",
    ")\n",
    "\n",
    "_b5._save_logs(df = _b5.df_files_priority_, name = 'auto_characteristics_priorities_mupta_ru', out = True)\n",
    "\n",
    "# Опционно\n",
    "df = _b5.df_files_priority_.rename(columns = {'Openness':'OPE', 'Conscientiousness':'CON', 'Extraversion': 'EXT', 'Agreeableness': 'AGR', 'Non-Neuroticism': 'NNEU'})\n",
    "columns_to_round = ['OPE', 'CON', 'EXT', 'AGR', 'NNEU']\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": "78e820f4-3f3d-43d2-b9b8-76d4b21efb78",
   "metadata": {},
   "source": [
    "#### Прогнозирование потребительских предпочтений на характеристики мобильного устройства\n",
    "\n",
    "В качестве примера предлагается использование коэффициентов корреляции между персональными качествами человека и характеристиками мобильного устройства, представленными в статье:\n",
    "\n",
    "1) Peltonen E., Sharmila P., Asare K. O., Visuri A., Lagerspetz E., Ferreira D. (2020). When phones get personal: Predicting Big Five personality traits from application usage // Pervasive and Mobile Computing. – 2020. – Vol. 69. – 101269."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "069f1f9d-18c1-42a8-89b9-0f9659544210",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Trait</th>\n",
       "      <th>Communication</th>\n",
       "      <th>Game Action</th>\n",
       "      <th>Game Board</th>\n",
       "      <th>Game Casino</th>\n",
       "      <th>Game Educational</th>\n",
       "      <th>Game Simulation</th>\n",
       "      <th>Game Trivia</th>\n",
       "      <th>Entertainment</th>\n",
       "      <th>Finance</th>\n",
       "      <th>Health and Fitness</th>\n",
       "      <th>Media and Video</th>\n",
       "      <th>Music and Audio</th>\n",
       "      <th>News and Magazines</th>\n",
       "      <th>Personalisation</th>\n",
       "      <th>Travel and Local</th>\n",
       "      <th>Weather</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ID</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "      <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>0.118</td>\n",
       "      <td>0.056</td>\n",
       "      <td>0.079</td>\n",
       "      <td>0.342</td>\n",
       "      <td>0.027</td>\n",
       "      <td>0.104</td>\n",
       "      <td>0.026</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.006</td>\n",
       "      <td>0.002</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.001</td>\n",
       "      <td>0.004</td>\n",
       "      <td>0.002</td>\n",
       "      <td>0.004</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Conscientiousness</td>\n",
       "      <td>0.119</td>\n",
       "      <td>0.043</td>\n",
       "      <td>0.107</td>\n",
       "      <td>0.448</td>\n",
       "      <td>0.039</td>\n",
       "      <td>0.012</td>\n",
       "      <td>0.119</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.005</td>\n",
       "      <td>0.001</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.002</td>\n",
       "      <td>0.002</td>\n",
       "      <td>0.001</td>\n",
       "      <td>0.001</td>\n",
       "      <td>0.003</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Extraversion</td>\n",
       "      <td>0.246</td>\n",
       "      <td>0.182</td>\n",
       "      <td>0.211</td>\n",
       "      <td>0.311</td>\n",
       "      <td>0.102</td>\n",
       "      <td>0.165</td>\n",
       "      <td>0.223</td>\n",
       "      <td>0.001</td>\n",
       "      <td>0.003</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.001</td>\n",
       "      <td>0.001</td>\n",
       "      <td>0.001</td>\n",
       "      <td>0.004</td>\n",
       "      <td>0.009</td>\n",
       "      <td>0.003</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Agreeableness</td>\n",
       "      <td>0.218</td>\n",
       "      <td>0.104</td>\n",
       "      <td>0.164</td>\n",
       "      <td>0.284</td>\n",
       "      <td>0.165</td>\n",
       "      <td>0.122</td>\n",
       "      <td>0.162</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.003</td>\n",
       "      <td>0.001</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.002</td>\n",
       "      <td>0.002</td>\n",
       "      <td>0.001</td>\n",
       "      <td>0.004</td>\n",
       "      <td>0.003</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Non-Neuroticism</td>\n",
       "      <td>0.046</td>\n",
       "      <td>0.047</td>\n",
       "      <td>0.125</td>\n",
       "      <td>0.515</td>\n",
       "      <td>0.272</td>\n",
       "      <td>0.179</td>\n",
       "      <td>0.214</td>\n",
       "      <td>0.002</td>\n",
       "      <td>0.030</td>\n",
       "      <td>0.001</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.005</td>\n",
       "      <td>0.003</td>\n",
       "      <td>0.008</td>\n",
       "      <td>0.004</td>\n",
       "      <td>0.007</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                Trait  Communication  Game Action  Game Board  Game Casino  \\\n",
       "ID                                                                           \n",
       "1            Openness          0.118        0.056       0.079        0.342   \n",
       "2   Conscientiousness          0.119        0.043       0.107        0.448   \n",
       "3        Extraversion          0.246        0.182       0.211        0.311   \n",
       "4       Agreeableness          0.218        0.104       0.164        0.284   \n",
       "5     Non-Neuroticism          0.046        0.047       0.125        0.515   \n",
       "\n",
       "    Game Educational  Game Simulation  Game Trivia  Entertainment  Finance  \\\n",
       "ID                                                                           \n",
       "1              0.027            0.104        0.026          0.000    0.006   \n",
       "2              0.039            0.012        0.119          0.000    0.005   \n",
       "3              0.102            0.165        0.223          0.001    0.003   \n",
       "4              0.165            0.122        0.162          0.000    0.003   \n",
       "5              0.272            0.179        0.214          0.002    0.030   \n",
       "\n",
       "    Health and Fitness  Media and Video  Music and Audio  News and Magazines  \\\n",
       "ID                                                                             \n",
       "1                0.002            0.000            0.000               0.001   \n",
       "2                0.001            0.000            0.002               0.002   \n",
       "3                0.000            0.001            0.001               0.001   \n",
       "4                0.001            0.000            0.002               0.002   \n",
       "5                0.001            0.000            0.005               0.003   \n",
       "\n",
       "    Personalisation  Travel and Local  Weather  \n",
       "ID                                              \n",
       "1             0.004             0.002    0.004  \n",
       "2             0.001             0.001    0.003  \n",
       "3             0.004             0.009    0.003  \n",
       "4             0.001             0.004    0.003  \n",
       "5             0.008             0.004    0.007  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Загрузка датафрейма с коэффициентами корреляции\n",
    "url = 'https://download.sberdisk.ru/download/file/478676690?token=7KcAxPqMpWiYQnx&filename=divice_characteristics.csv'\n",
    "df_divice_characteristics = pd.read_csv(url)\n",
    "\n",
    "df_divice_characteristics.index.name = 'ID'\n",
    "df_divice_characteristics.index += 1\n",
    "df_divice_characteristics.index = df_divice_characteristics.index.map(str)\n",
    "\n",
    "df_divice_characteristics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "08e50bb5-6da8-493f-9f3d-3bb1dbf0f5dc",
   "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>Path</th>\n",
       "      <th>OPE</th>\n",
       "      <th>CON</th>\n",
       "      <th>EXT</th>\n",
       "      <th>AGR</th>\n",
       "      <th>NNEU</th>\n",
       "      <th>Priority 1</th>\n",
       "      <th>Priority 2</th>\n",
       "      <th>Priority 3</th>\n",
       "      <th>Trait importance 1</th>\n",
       "      <th>Trait importance 2</th>\n",
       "      <th>Trait importance 3</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Person ID</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <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>0.758</td>\n",
       "      <td>0.693</td>\n",
       "      <td>0.650</td>\n",
       "      <td>0.745</td>\n",
       "      <td>0.489</td>\n",
       "      <td>Game Casino</td>\n",
       "      <td>Communication</td>\n",
       "      <td>Game Board</td>\n",
       "      <td>Extraversion</td>\n",
       "      <td>Agreeableness</td>\n",
       "      <td>Conscientiousness</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>speaker_06_center_83.mov</td>\n",
       "      <td>0.682</td>\n",
       "      <td>0.654</td>\n",
       "      <td>0.607</td>\n",
       "      <td>0.731</td>\n",
       "      <td>0.418</td>\n",
       "      <td>Game Casino</td>\n",
       "      <td>Communication</td>\n",
       "      <td>Game Board</td>\n",
       "      <td>Agreeableness</td>\n",
       "      <td>Extraversion</td>\n",
       "      <td>Conscientiousness</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>speaker_07_center_83.mov</td>\n",
       "      <td>0.666</td>\n",
       "      <td>0.657</td>\n",
       "      <td>0.568</td>\n",
       "      <td>0.685</td>\n",
       "      <td>0.378</td>\n",
       "      <td>Game Casino</td>\n",
       "      <td>Communication</td>\n",
       "      <td>Game Board</td>\n",
       "      <td>Agreeableness</td>\n",
       "      <td>Conscientiousness</td>\n",
       "      <td>Extraversion</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>speaker_10_center_83.mov</td>\n",
       "      <td>0.694</td>\n",
       "      <td>0.596</td>\n",
       "      <td>0.571</td>\n",
       "      <td>0.662</td>\n",
       "      <td>0.349</td>\n",
       "      <td>Game Casino</td>\n",
       "      <td>Communication</td>\n",
       "      <td>Game Board</td>\n",
       "      <td>Agreeableness</td>\n",
       "      <td>Extraversion</td>\n",
       "      <td>Conscientiousness</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>speaker_11_center_83.mov</td>\n",
       "      <td>0.713</td>\n",
       "      <td>0.595</td>\n",
       "      <td>0.572</td>\n",
       "      <td>0.717</td>\n",
       "      <td>0.378</td>\n",
       "      <td>Game Casino</td>\n",
       "      <td>Communication</td>\n",
       "      <td>Game Board</td>\n",
       "      <td>Agreeableness</td>\n",
       "      <td>Extraversion</td>\n",
       "      <td>Conscientiousness</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>speaker_15_center_83.mov</td>\n",
       "      <td>0.664</td>\n",
       "      <td>0.670</td>\n",
       "      <td>0.604</td>\n",
       "      <td>0.696</td>\n",
       "      <td>0.400</td>\n",
       "      <td>Game Casino</td>\n",
       "      <td>Communication</td>\n",
       "      <td>Game Board</td>\n",
       "      <td>Extraversion</td>\n",
       "      <td>Agreeableness</td>\n",
       "      <td>Conscientiousness</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>speaker_19_center_83.mov</td>\n",
       "      <td>0.761</td>\n",
       "      <td>0.653</td>\n",
       "      <td>0.651</td>\n",
       "      <td>0.789</td>\n",
       "      <td>0.460</td>\n",
       "      <td>Game Casino</td>\n",
       "      <td>Communication</td>\n",
       "      <td>Game Board</td>\n",
       "      <td>Agreeableness</td>\n",
       "      <td>Extraversion</td>\n",
       "      <td>Conscientiousness</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>speaker_23_center_83.mov</td>\n",
       "      <td>0.693</td>\n",
       "      <td>0.683</td>\n",
       "      <td>0.617</td>\n",
       "      <td>0.795</td>\n",
       "      <td>0.447</td>\n",
       "      <td>Game Casino</td>\n",
       "      <td>Communication</td>\n",
       "      <td>Game Board</td>\n",
       "      <td>Agreeableness</td>\n",
       "      <td>Extraversion</td>\n",
       "      <td>Conscientiousness</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>speaker_24_center_83.mov</td>\n",
       "      <td>0.706</td>\n",
       "      <td>0.658</td>\n",
       "      <td>0.611</td>\n",
       "      <td>0.697</td>\n",
       "      <td>0.412</td>\n",
       "      <td>Game Casino</td>\n",
       "      <td>Communication</td>\n",
       "      <td>Game Board</td>\n",
       "      <td>Extraversion</td>\n",
       "      <td>Agreeableness</td>\n",
       "      <td>Conscientiousness</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>speaker_27_center_83.mov</td>\n",
       "      <td>0.753</td>\n",
       "      <td>0.708</td>\n",
       "      <td>0.655</td>\n",
       "      <td>0.816</td>\n",
       "      <td>0.505</td>\n",
       "      <td>Game Casino</td>\n",
       "      <td>Communication</td>\n",
       "      <td>Game Board</td>\n",
       "      <td>Agreeableness</td>\n",
       "      <td>Extraversion</td>\n",
       "      <td>Conscientiousness</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                               Path    OPE    CON    EXT    AGR   NNEU  \\\n",
       "Person ID                                                                \n",
       "1          speaker_01_center_83.mov  0.758  0.693  0.650  0.745  0.489   \n",
       "2          speaker_06_center_83.mov  0.682  0.654  0.607  0.731  0.418   \n",
       "3          speaker_07_center_83.mov  0.666  0.657  0.568  0.685  0.378   \n",
       "4          speaker_10_center_83.mov  0.694  0.596  0.571  0.662  0.349   \n",
       "5          speaker_11_center_83.mov  0.713  0.595  0.572  0.717  0.378   \n",
       "6          speaker_15_center_83.mov  0.664  0.670  0.604  0.696  0.400   \n",
       "7          speaker_19_center_83.mov  0.761  0.653  0.651  0.789  0.460   \n",
       "8          speaker_23_center_83.mov  0.693  0.683  0.617  0.795  0.447   \n",
       "9          speaker_24_center_83.mov  0.706  0.658  0.611  0.697  0.412   \n",
       "10         speaker_27_center_83.mov  0.753  0.708  0.655  0.816  0.505   \n",
       "\n",
       "            Priority 1     Priority 2  Priority 3 Trait importance 1  \\\n",
       "Person ID                                                              \n",
       "1          Game Casino  Communication  Game Board       Extraversion   \n",
       "2          Game Casino  Communication  Game Board      Agreeableness   \n",
       "3          Game Casino  Communication  Game Board      Agreeableness   \n",
       "4          Game Casino  Communication  Game Board      Agreeableness   \n",
       "5          Game Casino  Communication  Game Board      Agreeableness   \n",
       "6          Game Casino  Communication  Game Board       Extraversion   \n",
       "7          Game Casino  Communication  Game Board      Agreeableness   \n",
       "8          Game Casino  Communication  Game Board      Agreeableness   \n",
       "9          Game Casino  Communication  Game Board       Extraversion   \n",
       "10         Game Casino  Communication  Game Board      Agreeableness   \n",
       "\n",
       "          Trait importance 2 Trait importance 3  \n",
       "Person ID                                        \n",
       "1              Agreeableness  Conscientiousness  \n",
       "2               Extraversion  Conscientiousness  \n",
       "3          Conscientiousness       Extraversion  \n",
       "4               Extraversion  Conscientiousness  \n",
       "5               Extraversion  Conscientiousness  \n",
       "6              Agreeableness  Conscientiousness  \n",
       "7               Extraversion  Conscientiousness  \n",
       "8               Extraversion  Conscientiousness  \n",
       "9              Agreeableness  Conscientiousness  \n",
       "10              Extraversion  Conscientiousness  "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "_b5._priority_calculation(\n",
    "    correlation_coefficients = df_divice_characteristics,\n",
    "    col_name_ocean = 'Trait',\n",
    "    threshold = 0.55,\n",
    "    number_priority = 3,\n",
    "    number_importance_traits = 3,\n",
    "    out = True\n",
    ")\n",
    "\n",
    "_b5._save_logs(df = _b5.df_files_priority_, name = 'divice_characteristics_priorities_mupta_ru', out = True)\n",
    "\n",
    "# Опционно\n",
    "df = _b5.df_files_priority_.rename(columns = {'Openness':'OPE', 'Conscientiousness':'CON', 'Extraversion': 'EXT', 'Agreeableness': 'AGR', 'Non-Neuroticism': 'NNEU'})\n",
    "columns_to_round = ['OPE', 'CON', 'EXT', 'AGR', 'NNEU']\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": 12,
   "id": "1b1f1294-6f09-4827-85c6-75ccc7fbd375",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/markdown": [
       "<span style=\"color:#666\">**[</span><span style=\"color:#1776D2\">2023-12-16 19:20:55</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\">2023-12-16 19:20:57</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": {
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       "    }\n",
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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",
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       "      <th>Person ID</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>speaker_01_center_83.mov</td>\n",
       "      <td>0.564985</td>\n",
       "      <td>0.539052</td>\n",
       "      <td>0.440615</td>\n",
       "      <td>0.59251</td>\n",
       "      <td>0.488763</td>\n",
       "    </tr>\n",
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       "      <th>2</th>\n",
       "      <td>speaker_06_center_83.mov</td>\n",
       "      <td>0.650774</td>\n",
       "      <td>0.663849</td>\n",
       "      <td>0.607308</td>\n",
       "      <td>0.643847</td>\n",
       "      <td>0.620627</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>speaker_07_center_83.mov</td>\n",
       "      <td>0.435976</td>\n",
       "      <td>0.486683</td>\n",
       "      <td>0.313828</td>\n",
       "      <td>0.415446</td>\n",
       "      <td>0.396618</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>speaker_10_center_83.mov</td>\n",
       "      <td>0.498542</td>\n",
       "      <td>0.511243</td>\n",
       "      <td>0.412592</td>\n",
       "      <td>0.468947</td>\n",
       "      <td>0.44399</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>speaker_11_center_83.mov</td>\n",
       "      <td>0.394776</td>\n",
       "      <td>0.341608</td>\n",
       "      <td>0.327082</td>\n",
       "      <td>0.427304</td>\n",
       "      <td>0.354936</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>speaker_15_center_83.mov</td>\n",
       "      <td>0.566107</td>\n",
       "      <td>0.543811</td>\n",
       "      <td>0.492766</td>\n",
       "      <td>0.587411</td>\n",
       "      <td>0.499433</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>speaker_19_center_83.mov</td>\n",
       "      <td>0.506271</td>\n",
       "      <td>0.438215</td>\n",
       "      <td>0.430894</td>\n",
       "      <td>0.456177</td>\n",
       "      <td>0.44075</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>speaker_23_center_83.mov</td>\n",
       "      <td>0.486463</td>\n",
       "      <td>0.521755</td>\n",
       "      <td>0.309894</td>\n",
       "      <td>0.432291</td>\n",
       "      <td>0.433601</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>speaker_24_center_83.mov</td>\n",
       "      <td>0.417404</td>\n",
       "      <td>0.473339</td>\n",
       "      <td>0.320714</td>\n",
       "      <td>0.445086</td>\n",
       "      <td>0.414649</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>speaker_27_center_83.mov</td>\n",
       "      <td>0.526112</td>\n",
       "      <td>0.661107</td>\n",
       "      <td>0.443167</td>\n",
       "      <td>0.558965</td>\n",
       "      <td>0.554224</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  0.564985          0.539052     0.440615   \n",
       "2          speaker_06_center_83.mov  0.650774          0.663849     0.607308   \n",
       "3          speaker_07_center_83.mov  0.435976          0.486683     0.313828   \n",
       "4          speaker_10_center_83.mov  0.498542          0.511243     0.412592   \n",
       "5          speaker_11_center_83.mov  0.394776          0.341608     0.327082   \n",
       "6          speaker_15_center_83.mov  0.566107          0.543811     0.492766   \n",
       "7          speaker_19_center_83.mov  0.506271          0.438215     0.430894   \n",
       "8          speaker_23_center_83.mov  0.486463          0.521755     0.309894   \n",
       "9          speaker_24_center_83.mov  0.417404          0.473339     0.320714   \n",
       "10         speaker_27_center_83.mov  0.526112          0.661107     0.443167   \n",
       "\n",
       "          Agreeableness Non-Neuroticism  \n",
       "Person ID                                \n",
       "1               0.59251        0.488763  \n",
       "2              0.643847        0.620627  \n",
       "3              0.415446        0.396618  \n",
       "4              0.468947         0.44399  \n",
       "5              0.427304        0.354936  \n",
       "6              0.587411        0.499433  \n",
       "7              0.456177         0.44075  \n",
       "8              0.432291        0.433601  \n",
       "9              0.445086        0.414649  \n",
       "10             0.558965        0.554224  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "<span style=\"color:#666\">**[</span><span style=\"color:#1776D2\">2023-12-16 19:20:57</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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       "<div>\n",
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       "\n",
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       "\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>0.1727</td>\n",
       "      <td>0.1672</td>\n",
       "      <td>0.1661</td>\n",
       "      <td>0.2579</td>\n",
       "      <td>0.107</td>\n",
       "      <td>0.1742</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Accuracy</th>\n",
       "      <td>0.8273</td>\n",
       "      <td>0.8328</td>\n",
       "      <td>0.8339</td>\n",
       "      <td>0.7421</td>\n",
       "      <td>0.893</td>\n",
       "      <td>0.8258</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          Openness  Conscientiousness  Extraversion  Agreeableness  \\\n",
       "Metrics                                                              \n",
       "MAE         0.1727             0.1672        0.1661         0.2579   \n",
       "Accuracy    0.8273             0.8328        0.8339         0.7421   \n",
       "\n",
       "          Non-Neuroticism    Mean  \n",
       "Metrics                            \n",
       "MAE                 0.107  0.1742  \n",
       "Accuracy            0.893  0.8258  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "<span style=\"color:#666\">**[</span><span style=\"color:#1776D2\">2023-12-16 19:20:57</span><span style=\"color:#666\">]</span> <span style=\"color:#666\">Средняя средних абсолютных ошибок: <span style=\"color:#1776D2\">0.1742</span>, средняя точность: <span style=\"color:#1776D2\">0.8258</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: 379.936 сек. ---**</span>"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
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    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 12,
     "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']['sberdisk']\n",
    "res_load_model_weights_hc = _b5.load_audio_model_weights_hc(url = url)\n",
    "\n",
    "url = _b5.weights_for_big5_['audio'][corpus]['nn']['sberdisk']\n",
    "res_load_model_weights_nn = _b5.load_audio_model_weights_nn(url = url)\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']['sberdisk']\n",
    "res_load_model_weights_hc = _b5.load_video_model_weights_hc(url = url)\n",
    "\n",
    "url = _b5.weights_for_big5_['video'][corpus]['fe']['sberdisk']\n",
    "res_load_model_weights_deep_fe = _b5.load_video_model_weights_deep_fe(url = url)\n",
    "\n",
    "url = _b5.weights_for_big5_['video'][corpus]['nn']['sberdisk']\n",
    "res_load_model_weights_nn = _b5.load_video_model_weights_nn(url = url)\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']['sberdisk']\n",
    "res_load_text_model_weights_hc_fi = _b5.load_text_model_weights_hc(url = url)\n",
    "\n",
    "url = _b5.weights_for_big5_['text'][corpus]['nn']['sberdisk']\n",
    "res_load_text_model_weights_nn_fi = _b5.load_text_model_weights_nn(url = url)\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']['sberdisk']\n",
    "res_load_avt_model_weights_b5 = _b5.load_avt_model_weights_b5(url = url)\n",
    "\n",
    "PATH_TO_DIR = './video_MuPTA/'\n",
    "PATH_SAVE_VIDEO = './video_MuPTA/test/'\n",
    "\n",
    "_b5.path_to_save_ = PATH_SAVE_VIDEO\n",
    "\n",
    "# Загрузка 10 тестовых аудиовидеозаписей из корпуса MuPTA\n",
    "# URL: https://hci.nw.ru/en/pages/mupta-corpus\n",
    "domain = 'https://download.sberdisk.ru/download/file/'\n",
    "tets_name_files = [\n",
    "    '477995979?token=2cvyk7CS0mHx2MJ&filename=speaker_06_center_83.mov',\n",
    "    '477995980?token=jGPtBPS69uzFU6Y&filename=speaker_01_center_83.mov',\n",
    "    '477995967?token=zCaRbNB6ht5wMPq&filename=speaker_11_center_83.mov',\n",
    "    '477995966?token=B1rbinDYRQKrI3T&filename=speaker_15_center_83.mov',\n",
    "    '477995978?token=dEpVDtZg1EQiEQ9&filename=speaker_07_center_83.mov',\n",
    "    '477995961?token=o1hVjw8G45q9L9Z&filename=speaker_19_center_83.mov',\n",
    "    '477995964?token=5K220Aqf673VHPq&filename=speaker_23_center_83.mov',\n",
    "    '477995965?token=v1LVD2KT1cU7Lpb&filename=speaker_24_center_83.mov',\n",
    "    '477995962?token=tmaSGyyWLA6XCy9&filename=speaker_27_center_83.mov',\n",
    "    '477995963?token=bTpo96qNDPcwGqb&filename=speaker_10_center_83.mov',\n",
    "]\n",
    "\n",
    "for curr_files in tets_name_files:\n",
    "    _b5.download_file_from_url(url = domain + curr_files, out = True)\n",
    "\n",
    "# Получение прогнозов\n",
    "_b5.path_to_dataset_ = PATH_TO_DIR # Директория набора данных\n",
    "_b5.ext_ = ['.mov'] # Расширения искомых файлов\n",
    "\n",
    "# Полный путь к файлу с верными предсказаниями для подсчета точности\n",
    "url_accuracy = _b5.true_traits_['mupta']['sberdisk']\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 прогнозирования потребительских предпочтений в промышленных товарах необходимо знать коэффициенты корреляции, определяющие взаимосвязь между персональными качествами личности человека и предпочтениями в товарах или услугах.\n",
    "\n",
    "В качестве примера предлагается использование коэффициентов корреляции между персональными качествами человека и характеристиками автомобилей, представленными в статье:\n",
    "\n",
    "1) O'Connor P. J. et al. What Drives Consumer Automobile Choice? Investigating Personality Trait Predictors of Vehicle Preference Factors // Personality and Individual Differences. – 2022. – Vol. 184. – pp. 111220.\n",
    "\n",
    "Пользователь может установить свои коэффициенты корреляции."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3e9151a3-d8bd-4c76-82b2-4ae935ac6ea0",
   "metadata": {},
   "source": [
    "#### Прогнозирование потребительских предпочтений на характеристики атомобиля"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "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>Trait</th>\n",
       "      <th>Performance</th>\n",
       "      <th>Classic car features</th>\n",
       "      <th>Luxury additions</th>\n",
       "      <th>Fashion and attention</th>\n",
       "      <th>Recreation</th>\n",
       "      <th>Technology</th>\n",
       "      <th>Family friendly</th>\n",
       "      <th>Safe and reliable</th>\n",
       "      <th>Practical and easy to use</th>\n",
       "      <th>Economical/low cost</th>\n",
       "      <th>Basic features</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",
       "      <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>0.020000</td>\n",
       "      <td>-0.033333</td>\n",
       "      <td>-0.030000</td>\n",
       "      <td>-0.050000</td>\n",
       "      <td>0.033333</td>\n",
       "      <td>0.013333</td>\n",
       "      <td>-0.030000</td>\n",
       "      <td>0.136667</td>\n",
       "      <td>0.106667</td>\n",
       "      <td>0.093333</td>\n",
       "      <td>0.006667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Conscientiousness</td>\n",
       "      <td>0.013333</td>\n",
       "      <td>-0.193333</td>\n",
       "      <td>-0.063333</td>\n",
       "      <td>-0.096667</td>\n",
       "      <td>-0.096667</td>\n",
       "      <td>0.086667</td>\n",
       "      <td>-0.063333</td>\n",
       "      <td>0.280000</td>\n",
       "      <td>0.180000</td>\n",
       "      <td>0.130000</td>\n",
       "      <td>0.143333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Extraversion</td>\n",
       "      <td>0.133333</td>\n",
       "      <td>0.060000</td>\n",
       "      <td>0.106667</td>\n",
       "      <td>0.123333</td>\n",
       "      <td>0.126667</td>\n",
       "      <td>0.120000</td>\n",
       "      <td>0.090000</td>\n",
       "      <td>0.136667</td>\n",
       "      <td>0.043333</td>\n",
       "      <td>0.073333</td>\n",
       "      <td>0.050000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Agreeableness</td>\n",
       "      <td>-0.036667</td>\n",
       "      <td>-0.193333</td>\n",
       "      <td>-0.133333</td>\n",
       "      <td>-0.133333</td>\n",
       "      <td>-0.090000</td>\n",
       "      <td>0.046667</td>\n",
       "      <td>-0.016667</td>\n",
       "      <td>0.240000</td>\n",
       "      <td>0.160000</td>\n",
       "      <td>0.120000</td>\n",
       "      <td>0.083333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Non-Neuroticism</td>\n",
       "      <td>0.016667</td>\n",
       "      <td>-0.006667</td>\n",
       "      <td>-0.010000</td>\n",
       "      <td>-0.006667</td>\n",
       "      <td>-0.033333</td>\n",
       "      <td>0.046667</td>\n",
       "      <td>-0.023333</td>\n",
       "      <td>0.093333</td>\n",
       "      <td>0.046667</td>\n",
       "      <td>0.046667</td>\n",
       "      <td>-0.040000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                Trait  Performance  Classic car features  Luxury additions  \\\n",
       "ID                                                                           \n",
       "1            Openness     0.020000             -0.033333         -0.030000   \n",
       "2   Conscientiousness     0.013333             -0.193333         -0.063333   \n",
       "3        Extraversion     0.133333              0.060000          0.106667   \n",
       "4       Agreeableness    -0.036667             -0.193333         -0.133333   \n",
       "5     Non-Neuroticism     0.016667             -0.006667         -0.010000   \n",
       "\n",
       "    Fashion and attention  Recreation  Technology  Family friendly  \\\n",
       "ID                                                                   \n",
       "1               -0.050000    0.033333    0.013333        -0.030000   \n",
       "2               -0.096667   -0.096667    0.086667        -0.063333   \n",
       "3                0.123333    0.126667    0.120000         0.090000   \n",
       "4               -0.133333   -0.090000    0.046667        -0.016667   \n",
       "5               -0.006667   -0.033333    0.046667        -0.023333   \n",
       "\n",
       "    Safe and reliable  Practical and easy to use  Economical/low cost  \\\n",
       "ID                                                                      \n",
       "1            0.136667                   0.106667             0.093333   \n",
       "2            0.280000                   0.180000             0.130000   \n",
       "3            0.136667                   0.043333             0.073333   \n",
       "4            0.240000                   0.160000             0.120000   \n",
       "5            0.093333                   0.046667             0.046667   \n",
       "\n",
       "    Basic features  \n",
       "ID                  \n",
       "1         0.006667  \n",
       "2         0.143333  \n",
       "3         0.050000  \n",
       "4         0.083333  \n",
       "5        -0.040000  "
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Загрузка датафрейма с коэффициентами корреляции\n",
    "url = 'https://download.sberdisk.ru/download/file/478675818?token=EjfLMqOeK8cfnOu&filename=auto_characteristics.csv'\n",
    "df_correlation_coefficients = pd.read_csv(url)\n",
    "df_correlation_coefficients = pd.DataFrame(\n",
    "    df_correlation_coefficients.drop(['Style and performance', 'Safety and practicality'], axis = 1)\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": 14,
   "id": "6d095685-fda4-4943-9a0f-d0463a39798c",
   "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>Priority 1</th>\n",
       "      <th>Priority 2</th>\n",
       "      <th>Priority 3</th>\n",
       "      <th>Trait importance 1</th>\n",
       "      <th>Trait importance 2</th>\n",
       "      <th>Trait importance 3</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Person ID</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <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>0.565</td>\n",
       "      <td>0.539</td>\n",
       "      <td>0.441</td>\n",
       "      <td>0.593</td>\n",
       "      <td>0.489</td>\n",
       "      <td>Practical and easy to use</td>\n",
       "      <td>Economical/low cost</td>\n",
       "      <td>Family friendly</td>\n",
       "      <td>Agreeableness</td>\n",
       "      <td>Openness</td>\n",
       "      <td>Non-Neuroticism</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>speaker_06_center_83.mov</td>\n",
       "      <td>0.651</td>\n",
       "      <td>0.664</td>\n",
       "      <td>0.607</td>\n",
       "      <td>0.644</td>\n",
       "      <td>0.621</td>\n",
       "      <td>Safe and reliable</td>\n",
       "      <td>Practical and easy to use</td>\n",
       "      <td>Economical/low cost</td>\n",
       "      <td>Conscientiousness</td>\n",
       "      <td>Agreeableness</td>\n",
       "      <td>Openness</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>speaker_07_center_83.mov</td>\n",
       "      <td>0.436</td>\n",
       "      <td>0.487</td>\n",
       "      <td>0.314</td>\n",
       "      <td>0.415</td>\n",
       "      <td>0.397</td>\n",
       "      <td>Classic car features</td>\n",
       "      <td>Fashion and attention</td>\n",
       "      <td>Luxury additions</td>\n",
       "      <td>Agreeableness</td>\n",
       "      <td>Conscientiousness</td>\n",
       "      <td>Openness</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>speaker_10_center_83.mov</td>\n",
       "      <td>0.499</td>\n",
       "      <td>0.511</td>\n",
       "      <td>0.413</td>\n",
       "      <td>0.469</td>\n",
       "      <td>0.444</td>\n",
       "      <td>Classic car features</td>\n",
       "      <td>Fashion and attention</td>\n",
       "      <td>Luxury additions</td>\n",
       "      <td>Agreeableness</td>\n",
       "      <td>Conscientiousness</td>\n",
       "      <td>Openness</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>speaker_11_center_83.mov</td>\n",
       "      <td>0.395</td>\n",
       "      <td>0.342</td>\n",
       "      <td>0.327</td>\n",
       "      <td>0.427</td>\n",
       "      <td>0.355</td>\n",
       "      <td>Classic car features</td>\n",
       "      <td>Fashion and attention</td>\n",
       "      <td>Luxury additions</td>\n",
       "      <td>Agreeableness</td>\n",
       "      <td>Conscientiousness</td>\n",
       "      <td>Openness</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>speaker_15_center_83.mov</td>\n",
       "      <td>0.566</td>\n",
       "      <td>0.544</td>\n",
       "      <td>0.493</td>\n",
       "      <td>0.587</td>\n",
       "      <td>0.499</td>\n",
       "      <td>Practical and easy to use</td>\n",
       "      <td>Economical/low cost</td>\n",
       "      <td>Family friendly</td>\n",
       "      <td>Agreeableness</td>\n",
       "      <td>Openness</td>\n",
       "      <td>Non-Neuroticism</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>speaker_19_center_83.mov</td>\n",
       "      <td>0.506</td>\n",
       "      <td>0.438</td>\n",
       "      <td>0.431</td>\n",
       "      <td>0.456</td>\n",
       "      <td>0.441</td>\n",
       "      <td>Classic car features</td>\n",
       "      <td>Fashion and attention</td>\n",
       "      <td>Luxury additions</td>\n",
       "      <td>Agreeableness</td>\n",
       "      <td>Conscientiousness</td>\n",
       "      <td>Openness</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>speaker_23_center_83.mov</td>\n",
       "      <td>0.486</td>\n",
       "      <td>0.522</td>\n",
       "      <td>0.310</td>\n",
       "      <td>0.432</td>\n",
       "      <td>0.434</td>\n",
       "      <td>Classic car features</td>\n",
       "      <td>Fashion and attention</td>\n",
       "      <td>Luxury additions</td>\n",
       "      <td>Agreeableness</td>\n",
       "      <td>Conscientiousness</td>\n",
       "      <td>Openness</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>speaker_24_center_83.mov</td>\n",
       "      <td>0.417</td>\n",
       "      <td>0.473</td>\n",
       "      <td>0.321</td>\n",
       "      <td>0.445</td>\n",
       "      <td>0.415</td>\n",
       "      <td>Classic car features</td>\n",
       "      <td>Fashion and attention</td>\n",
       "      <td>Luxury additions</td>\n",
       "      <td>Agreeableness</td>\n",
       "      <td>Conscientiousness</td>\n",
       "      <td>Openness</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>speaker_27_center_83.mov</td>\n",
       "      <td>0.526</td>\n",
       "      <td>0.661</td>\n",
       "      <td>0.443</td>\n",
       "      <td>0.559</td>\n",
       "      <td>0.554</td>\n",
       "      <td>Safe and reliable</td>\n",
       "      <td>Practical and easy to use</td>\n",
       "      <td>Economical/low cost</td>\n",
       "      <td>Conscientiousness</td>\n",
       "      <td>Agreeableness</td>\n",
       "      <td>Non-Neuroticism</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                               Path    OPE    CON    EXT    AGR   NNEU  \\\n",
       "Person ID                                                                \n",
       "1          speaker_01_center_83.mov  0.565  0.539  0.441  0.593  0.489   \n",
       "2          speaker_06_center_83.mov  0.651  0.664  0.607  0.644  0.621   \n",
       "3          speaker_07_center_83.mov  0.436  0.487  0.314  0.415  0.397   \n",
       "4          speaker_10_center_83.mov  0.499  0.511  0.413  0.469  0.444   \n",
       "5          speaker_11_center_83.mov  0.395  0.342  0.327  0.427  0.355   \n",
       "6          speaker_15_center_83.mov  0.566  0.544  0.493  0.587  0.499   \n",
       "7          speaker_19_center_83.mov  0.506  0.438  0.431  0.456  0.441   \n",
       "8          speaker_23_center_83.mov  0.486  0.522  0.310  0.432  0.434   \n",
       "9          speaker_24_center_83.mov  0.417  0.473  0.321  0.445  0.415   \n",
       "10         speaker_27_center_83.mov  0.526  0.661  0.443  0.559  0.554   \n",
       "\n",
       "                          Priority 1                 Priority 2  \\\n",
       "Person ID                                                         \n",
       "1          Practical and easy to use        Economical/low cost   \n",
       "2                  Safe and reliable  Practical and easy to use   \n",
       "3               Classic car features      Fashion and attention   \n",
       "4               Classic car features      Fashion and attention   \n",
       "5               Classic car features      Fashion and attention   \n",
       "6          Practical and easy to use        Economical/low cost   \n",
       "7               Classic car features      Fashion and attention   \n",
       "8               Classic car features      Fashion and attention   \n",
       "9               Classic car features      Fashion and attention   \n",
       "10                 Safe and reliable  Practical and easy to use   \n",
       "\n",
       "                    Priority 3 Trait importance 1 Trait importance 2  \\\n",
       "Person ID                                                              \n",
       "1              Family friendly      Agreeableness           Openness   \n",
       "2          Economical/low cost  Conscientiousness      Agreeableness   \n",
       "3             Luxury additions      Agreeableness  Conscientiousness   \n",
       "4             Luxury additions      Agreeableness  Conscientiousness   \n",
       "5             Luxury additions      Agreeableness  Conscientiousness   \n",
       "6              Family friendly      Agreeableness           Openness   \n",
       "7             Luxury additions      Agreeableness  Conscientiousness   \n",
       "8             Luxury additions      Agreeableness  Conscientiousness   \n",
       "9             Luxury additions      Agreeableness  Conscientiousness   \n",
       "10         Economical/low cost  Conscientiousness      Agreeableness   \n",
       "\n",
       "          Trait importance 3  \n",
       "Person ID                     \n",
       "1            Non-Neuroticism  \n",
       "2                   Openness  \n",
       "3                   Openness  \n",
       "4                   Openness  \n",
       "5                   Openness  \n",
       "6            Non-Neuroticism  \n",
       "7                   Openness  \n",
       "8                   Openness  \n",
       "9                   Openness  \n",
       "10           Non-Neuroticism  "
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "_b5._priority_calculation(\n",
    "    correlation_coefficients = df_correlation_coefficients,\n",
    "    col_name_ocean = 'Trait',\n",
    "    threshold = 0.55,\n",
    "    number_priority = 3,\n",
    "    number_importance_traits = 3,\n",
    "    out = False\n",
    ")\n",
    "\n",
    "_b5._save_logs(df = _b5.df_files_priority_, name = 'auto_characteristics_priorities_mupta_en', out = True)\n",
    "\n",
    "# Опционно\n",
    "df = _b5.df_files_priority_.rename(columns = {'Openness':'OPE', 'Conscientiousness':'CON', 'Extraversion': 'EXT', 'Agreeableness': 'AGR', 'Non-Neuroticism': 'NNEU'})\n",
    "columns_to_round = ['OPE', 'CON', 'EXT', 'AGR', 'NNEU']\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": "7343080a-898e-4718-8d44-26f787c6cc24",
   "metadata": {},
   "source": [
    "#### Прогнозирование потребительских предпочтений на характеристики мобильного устройства\n",
    "\n",
    "В качестве примера предлагается использование коэффициентов корреляции между персональными качествами человека и характеристиками мобильного устройства, представленными в статье:\n",
    "\n",
    "1) Peltonen E., Sharmila P., Asare K. O., Visuri A., Lagerspetz E., Ferreira D. (2020). When phones get personal: Predicting Big Five personality traits from application usage // Pervasive and Mobile Computing. – 2020. – Vol. 69. – 101269."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "bacc7c56-18f4-4575-9f9e-54a038bf5df8",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Trait</th>\n",
       "      <th>Communication</th>\n",
       "      <th>Game Action</th>\n",
       "      <th>Game Board</th>\n",
       "      <th>Game Casino</th>\n",
       "      <th>Game Educational</th>\n",
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       "      <th>Weather</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",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Openness</td>\n",
       "      <td>0.118</td>\n",
       "      <td>0.056</td>\n",
       "      <td>0.079</td>\n",
       "      <td>0.342</td>\n",
       "      <td>0.027</td>\n",
       "      <td>0.104</td>\n",
       "      <td>0.026</td>\n",
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       "      <td>0.006</td>\n",
       "      <td>0.002</td>\n",
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       "      <td>0.000</td>\n",
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       "      <td>0.002</td>\n",
       "      <td>0.004</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Conscientiousness</td>\n",
       "      <td>0.119</td>\n",
       "      <td>0.043</td>\n",
       "      <td>0.107</td>\n",
       "      <td>0.448</td>\n",
       "      <td>0.039</td>\n",
       "      <td>0.012</td>\n",
       "      <td>0.119</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.005</td>\n",
       "      <td>0.001</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.002</td>\n",
       "      <td>0.002</td>\n",
       "      <td>0.001</td>\n",
       "      <td>0.001</td>\n",
       "      <td>0.003</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Extraversion</td>\n",
       "      <td>0.246</td>\n",
       "      <td>0.182</td>\n",
       "      <td>0.211</td>\n",
       "      <td>0.311</td>\n",
       "      <td>0.102</td>\n",
       "      <td>0.165</td>\n",
       "      <td>0.223</td>\n",
       "      <td>0.001</td>\n",
       "      <td>0.003</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.001</td>\n",
       "      <td>0.001</td>\n",
       "      <td>0.001</td>\n",
       "      <td>0.004</td>\n",
       "      <td>0.009</td>\n",
       "      <td>0.003</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Agreeableness</td>\n",
       "      <td>0.218</td>\n",
       "      <td>0.104</td>\n",
       "      <td>0.164</td>\n",
       "      <td>0.284</td>\n",
       "      <td>0.165</td>\n",
       "      <td>0.122</td>\n",
       "      <td>0.162</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.003</td>\n",
       "      <td>0.001</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.002</td>\n",
       "      <td>0.002</td>\n",
       "      <td>0.001</td>\n",
       "      <td>0.004</td>\n",
       "      <td>0.003</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Non-Neuroticism</td>\n",
       "      <td>0.046</td>\n",
       "      <td>0.047</td>\n",
       "      <td>0.125</td>\n",
       "      <td>0.515</td>\n",
       "      <td>0.272</td>\n",
       "      <td>0.179</td>\n",
       "      <td>0.214</td>\n",
       "      <td>0.002</td>\n",
       "      <td>0.030</td>\n",
       "      <td>0.001</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.005</td>\n",
       "      <td>0.003</td>\n",
       "      <td>0.008</td>\n",
       "      <td>0.004</td>\n",
       "      <td>0.007</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                Trait  Communication  Game Action  Game Board  Game Casino  \\\n",
       "ID                                                                           \n",
       "1            Openness          0.118        0.056       0.079        0.342   \n",
       "2   Conscientiousness          0.119        0.043       0.107        0.448   \n",
       "3        Extraversion          0.246        0.182       0.211        0.311   \n",
       "4       Agreeableness          0.218        0.104       0.164        0.284   \n",
       "5     Non-Neuroticism          0.046        0.047       0.125        0.515   \n",
       "\n",
       "    Game Educational  Game Simulation  Game Trivia  Entertainment  Finance  \\\n",
       "ID                                                                           \n",
       "1              0.027            0.104        0.026          0.000    0.006   \n",
       "2              0.039            0.012        0.119          0.000    0.005   \n",
       "3              0.102            0.165        0.223          0.001    0.003   \n",
       "4              0.165            0.122        0.162          0.000    0.003   \n",
       "5              0.272            0.179        0.214          0.002    0.030   \n",
       "\n",
       "    Health and Fitness  Media and Video  Music and Audio  News and Magazines  \\\n",
       "ID                                                                             \n",
       "1                0.002            0.000            0.000               0.001   \n",
       "2                0.001            0.000            0.002               0.002   \n",
       "3                0.000            0.001            0.001               0.001   \n",
       "4                0.001            0.000            0.002               0.002   \n",
       "5                0.001            0.000            0.005               0.003   \n",
       "\n",
       "    Personalisation  Travel and Local  Weather  \n",
       "ID                                              \n",
       "1             0.004             0.002    0.004  \n",
       "2             0.001             0.001    0.003  \n",
       "3             0.004             0.009    0.003  \n",
       "4             0.001             0.004    0.003  \n",
       "5             0.008             0.004    0.007  "
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Загрузка датафрейма с коэффициентами корреляции\n",
    "url = 'https://download.sberdisk.ru/download/file/478676690?token=7KcAxPqMpWiYQnx&filename=divice_characteristics.csv'\n",
    "df_divice_characteristics = pd.read_csv(url)\n",
    "\n",
    "df_divice_characteristics.index.name = 'ID'\n",
    "df_divice_characteristics.index += 1\n",
    "df_divice_characteristics.index = df_divice_characteristics.index.map(str)\n",
    "\n",
    "df_divice_characteristics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "00356c08-6004-4fe2-b676-61301bebeff6",
   "metadata": {},
   "outputs": [
    {
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       "      <td>0.539</td>\n",
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       "      <td>0.489</td>\n",
       "      <td>Communication</td>\n",
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       "      <td>Media and Video</td>\n",
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       "      <td>Openness</td>\n",
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       "      <td>0.651</td>\n",
       "      <td>0.664</td>\n",
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       "      <td>0.395</td>\n",
       "      <td>0.342</td>\n",
       "      <td>0.327</td>\n",
       "      <td>0.427</td>\n",
       "      <td>0.355</td>\n",
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       "      <td>Health and Fitness</td>\n",
       "      <td>Conscientiousness</td>\n",
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       "      <td>0.566</td>\n",
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       "      <td>0.493</td>\n",
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       "      <td>Agreeableness</td>\n",
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       "      <td>0.438</td>\n",
       "      <td>0.431</td>\n",
       "      <td>0.456</td>\n",
       "      <td>0.441</td>\n",
       "      <td>Media and Video</td>\n",
       "      <td>Entertainment</td>\n",
       "      <td>Health and Fitness</td>\n",
       "      <td>Conscientiousness</td>\n",
       "      <td>Agreeableness</td>\n",
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      "text/plain": [
       "                               Path    OPE    CON    EXT    AGR   NNEU  \\\n",
       "Person ID                                                                \n",
       "1          speaker_01_center_83.mov  0.565  0.539  0.441  0.593  0.489   \n",
       "2          speaker_06_center_83.mov  0.651  0.664  0.607  0.644  0.621   \n",
       "3          speaker_07_center_83.mov  0.436  0.487  0.314  0.415  0.397   \n",
       "4          speaker_10_center_83.mov  0.499  0.511  0.413  0.469  0.444   \n",
       "5          speaker_11_center_83.mov  0.395  0.342  0.327  0.427  0.355   \n",
       "6          speaker_15_center_83.mov  0.566  0.544  0.493  0.587  0.499   \n",
       "7          speaker_19_center_83.mov  0.506  0.438  0.431  0.456  0.441   \n",
       "8          speaker_23_center_83.mov  0.486  0.522  0.310  0.432  0.434   \n",
       "9          speaker_24_center_83.mov  0.417  0.473  0.321  0.445  0.415   \n",
       "10         speaker_27_center_83.mov  0.526  0.661  0.443  0.559  0.554   \n",
       "\n",
       "                   Priority 1          Priority 2          Priority 3  \\\n",
       "Person ID                                                               \n",
       "1               Communication  Health and Fitness     Media and Video   \n",
       "2                 Game Casino       Communication         Game Trivia   \n",
       "3             Media and Video       Entertainment  Health and Fitness   \n",
       "4             Media and Video       Entertainment  Health and Fitness   \n",
       "5             Media and Video       Entertainment  Health and Fitness   \n",
       "6          Health and Fitness     Media and Video  News and Magazines   \n",
       "7             Media and Video       Entertainment  Health and Fitness   \n",
       "8             Media and Video       Entertainment  Health and Fitness   \n",
       "9             Media and Video       Entertainment  Health and Fitness   \n",
       "10                Game Casino    Game Educational         Game Trivia   \n",
       "\n",
       "          Trait importance 1 Trait importance 2 Trait importance 3  \n",
       "Person ID                                                           \n",
       "1              Agreeableness           Openness    Non-Neuroticism  \n",
       "2            Non-Neuroticism       Extraversion  Conscientiousness  \n",
       "3              Agreeableness  Conscientiousness       Extraversion  \n",
       "4              Agreeableness  Conscientiousness       Extraversion  \n",
       "5          Conscientiousness      Agreeableness       Extraversion  \n",
       "6              Agreeableness           Openness       Extraversion  \n",
       "7          Conscientiousness      Agreeableness       Extraversion  \n",
       "8              Agreeableness  Conscientiousness       Extraversion  \n",
       "9              Agreeableness  Conscientiousness       Extraversion  \n",
       "10           Non-Neuroticism  Conscientiousness      Agreeableness  "
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "_b5._priority_calculation(\n",
    "    correlation_coefficients = df_divice_characteristics,\n",
    "    col_name_ocean = 'Trait',\n",
    "    threshold = 0.55,\n",
    "    number_priority = 3,\n",
    "    number_importance_traits = 3,\n",
    "    out = True\n",
    ")\n",
    "\n",
    "_b5._save_logs(df = _b5.df_files_priority_, name = 'divice_characteristics_priorities_mupta_en', out = True)\n",
    "\n",
    "# Опционно\n",
    "df = _b5.df_files_priority_.rename(columns = {'Openness':'OPE', 'Conscientiousness':'CON', 'Extraversion': 'EXT', 'Agreeableness': 'AGR', 'Non-Neuroticism': 'NNEU'})\n",
    "columns_to_round = ['OPE', 'CON', 'EXT', 'AGR', 'NNEU']\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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