Solution of practical task 2

Task: Predicting consumer preferences for industrial goods

The solution of the practical task is performed in two stages. At the first stage it is necessary to use the OCEAN-AI library to obtain predictions (personality traits scores). The second step is to use the _priority_calculation method from the OCEAN-AI library to solve the presented practical task. Examples of the results of the work and implementation are presented below.

Thus, the OCEAN-AI library provides tools to analyze the personality traits of consumers, aiding in predicting their interests. This enables companies to tailor products and services more accurately to consumer preferences, enhancing uniqueness and personalization.

177780ac47774ad39c99def6d47cc9f8

3f382e65762e4a9caba265325794c5b2

FI V2

[2]:

# Импорт необходимых инструментов
import os
import pandas as pd

# Импорт модуля
from oceanai.modules.lab.build import Run

# Создание экземпляра класса
_b5 = Run()

# Настройка ядра
_b5.path_to_save_ = './models' # Директория для сохранения файла
_b5.chunk_size_ = 2000000      # Размер загрузки файла из сети за 1 шаг

corpus = 'fi'

# Формирование аудиомоделей
res_load_model_hc = _b5.load_audio_model_hc()
res_load_model_nn = _b5.load_audio_model_nn()

# Загрузка весов аудиомоделей
url = _b5.weights_for_big5_['audio'][corpus]['hc']['googledisk']
res_load_model_weights_hc = _b5.load_audio_model_weights_hc(url = url, force_reload = False)

url = _b5.weights_for_big5_['audio'][corpus]['nn']['googledisk']
res_load_model_weights_nn = _b5.load_audio_model_weights_nn(url = url, force_reload = False)

# Формирование видеомоделей
res_load_model_hc = _b5.load_video_model_hc(lang='en')
res_load_model_deep_fe = _b5.load_video_model_deep_fe()
res_load_model_nn = _b5.load_video_model_nn()

# Загрузка весов видеомоделей
url = _b5.weights_for_big5_['video'][corpus]['hc']['googledisk']
res_load_model_weights_hc = _b5.load_video_model_weights_hc(url = url, force_reload = False)

url = _b5.weights_for_big5_['video'][corpus]['fe']['googledisk']
res_load_model_weights_deep_fe = _b5.load_video_model_weights_deep_fe(url = url, force_reload = False)

url = _b5.weights_for_big5_['video'][corpus]['nn']['googledisk']
res_load_model_weights_nn = _b5.load_video_model_weights_nn(url = url, force_reload = False)

# Загрузка словаря с экспертными признаками (текстовая модальность)
res_load_text_features = _b5.load_text_features()

# Формирование текстовых моделей
res_setup_translation_model = _b5.setup_translation_model() # только для русского языка
res_setup_translation_model = _b5.setup_bert_encoder(force_reload = False)
res_load_text_model_hc_fi = _b5.load_text_model_hc(corpus=corpus)
res_load_text_model_nn_fi = _b5.load_text_model_nn(corpus=corpus)

# Загрузка весов текстовых моделей
url = _b5.weights_for_big5_['text'][corpus]['hc']['googledisk']
res_load_text_model_weights_hc_fi = _b5.load_text_model_weights_hc(url = url, force_reload = False)

url = _b5.weights_for_big5_['text'][corpus]['nn']['googledisk']
res_load_text_model_weights_nn_fi = _b5.load_text_model_weights_nn(url = url, force_reload = False)

# Формирование модели для мультимодального объединения информации
res_load_avt_model_b5 = _b5.load_avt_model_b5()

# Загрузка весов модели для мультимодального объединения информации
url = _b5.weights_for_big5_['avt'][corpus]['b5']['googledisk']
res_load_avt_model_weights_b5 = _b5.load_avt_model_weights_b5(url = url, force_reload = False)

PATH_TO_DIR = './video_FI/'
PATH_SAVE_VIDEO = './video_FI/test/'

_b5.path_to_save_ = PATH_SAVE_VIDEO

# Загрузка 10 тестовых аудиовидеозаписей из корпуса First Impression V2
# URL: https://chalearnlap.cvc.uab.cat/dataset/24/description/
domain = 'https://download.sberdisk.ru/download/file/'
tets_name_files = [
    '429713680?token=FqHdMLSSh7zYSZt&filename=_plk5k7PBEg.003.mp4',
    '429713681?token=Hz9b4lQkrLfic33&filename=be0DQawtVkE.002.mp4',
    '429713683?token=EgUXS9Xs8xHm5gz&filename=2d6btbaNdfo.000.mp4',
    '429713684?token=1U26753kmPYdIgt&filename=300gK3CnzW0.003.mp4',
    '429713685?token=LyigAWLTzDNwKJO&filename=300gK3CnzW0.001.mp4',
    '429713686?token=EpfRbCKHyuc4HPu&filename=cLaZxEf1nE4.004.mp4',
    '429713687?token=FNTkwqBr4jOS95l&filename=g24JGYuT74A.004.mp4',
    '429713688?token=qDT95nz7hfm2Nki&filename=JZNMxa3OKHY.000.mp4',
    '429713689?token=noLguEGXDpbcKhg&filename=nvlqJbHk_Lc.003.mp4',
    '429713679?token=9L7RQ0hgdJlcek6&filename=4vdJGgZpj4k.003.mp4'
]

for curr_files in tets_name_files:
    _b5.download_file_from_url(url = domain + curr_files, out = True)

# Получение прогнозов
_b5.path_to_dataset_ = PATH_TO_DIR # Директория набора данных
_b5.ext_ = ['.mp4'] # Расширения искомых файлов

# Полный путь к файлу с верными предсказаниями для подсчета точности
url_accuracy = _b5.true_traits_[corpus]['googledisk']

_b5.get_avt_predictions(url_accuracy = url_accuracy, lang = 'en')

[2024-10-10 18:10:50] Извлечение признаков (экспертных и нейросетевых) из текста …

[2024-10-10 18:10:50] Получение прогнозов и вычисление точности (мультимодальное объединение) …

    10 из 10 (100.0%) … GitHub:nbsphinx-math:OCEANAI\docs\source\user_guide:nbsphinx-math:notebooks\video_FI:nbsphinx-math:test_plk5k7PBEg.003.mp4 …

Path Openness Conscientiousness Extraversion Agreeableness Non-Neuroticism
Person ID
1 2d6btbaNdfo.000.mp4 0.618917 0.660694 0.477656 0.654437 0.601256
2 300gK3CnzW0.001.mp4 0.461732 0.413451 0.415706 0.498301 0.431224
3 300gK3CnzW0.003.mp4 0.468002 0.448618 0.371742 0.509602 0.453739
4 4vdJGgZpj4k.003.mp4 0.585348 0.616446 0.49443 0.605614 0.587017
5 be0DQawtVkE.002.mp4 0.680991 0.56602 0.553915 0.646545 0.64246
6 cLaZxEf1nE4.004.mp4 0.66342 0.551018 0.557912 0.585238 0.587174
7 g24JGYuT74A.004.mp4 0.590237 0.399273 0.409554 0.531861 0.507134
8 JZNMxa3OKHY.000.mp4 0.60577 0.523617 0.531137 0.594406 0.57984
9 nvlqJbHk_Lc.003.mp4 0.511002 0.464702 0.390882 0.443663 0.438811
10 _plk5k7PBEg.003.mp4 0.647606 0.610466 0.524718 0.61428 0.606428

[2024-10-10 18:10:50] Точность по отдельным персональным качествам личности человека …

Openness Conscientiousness Extraversion Agreeableness Non-Neuroticism Mean
Metrics
MAE 0.0735 0.0631 0.0914 0.0706 0.0691 0.0735
Accuracy 0.9265 0.9369 0.9086 0.9294 0.9309 0.9265

[2024-10-10 18:10:50] Средняя средних абсолютных ошибок: 0.0735, средняя точность: 0.9265 …

Лог файлы успешно сохранены …

— Время выполнения: 35.449 сек. —

[2]:

True

To predict consumer preferences for industrial goods, it is necessary to know the correlation coefficients that determine the relationship between personality traits and preferences in goods or services.

As an example, it is proposed to use the correlation coefficients between the personality traits and the characteristics of the cars presented in the article:

  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.

The user can set their own correlation coefficients.

Predicting consumer preferences for industrial goods on the example of car characteristics

[3]:

# Loading dataframe with correlation coefficients
url = 'https://download.sberdisk.ru/download/file/478675818?token=EjfLMqOeK8cfnOu&filename=auto_characteristics.csv'
df_correlation_coefficients = pd.read_csv(url)
df_correlation_coefficients = pd.DataFrame(
    df_correlation_coefficients.drop(['Style and performance', 'Safety and practicality'], axis = 1)
)
df_correlation_coefficients.index.name = 'ID'
df_correlation_coefficients.index += 1
df_correlation_coefficients.index = df_correlation_coefficients.index.map(str)

df_correlation_coefficients

[3]:
Trait Performance Classic car features Luxury additions Fashion and attention Recreation Technology Family friendly Safe and reliable Practical and easy to use Economical/low cost Basic features
ID
1 Openness 0.020000 -0.033333 -0.030000 -0.050000 0.033333 0.013333 -0.030000 0.136667 0.106667 0.093333 0.006667
2 Conscientiousness 0.013333 -0.193333 -0.063333 -0.096667 -0.096667 0.086667 -0.063333 0.280000 0.180000 0.130000 0.143333
3 Extraversion 0.133333 0.060000 0.106667 0.123333 0.126667 0.120000 0.090000 0.136667 0.043333 0.073333 0.050000
4 Agreeableness -0.036667 -0.193333 -0.133333 -0.133333 -0.090000 0.046667 -0.016667 0.240000 0.160000 0.120000 0.083333
5 Non-Neuroticism 0.016667 -0.006667 -0.010000 -0.006667 -0.033333 0.046667 -0.023333 0.093333 0.046667 0.046667 -0.040000 
[4]:

_b5._priority_calculation(
    correlation_coefficients = df_correlation_coefficients,
    col_name_ocean = 'Trait',
    threshold = 0.55,
    number_priority = 3,
    number_importance_traits = 3,
    out = False
)

_b5._save_logs(df = _b5.df_files_priority_, name = 'auto_characteristics_priorities_fi_en', out = True)

# Optional
df = _b5.df_files_priority_.rename(columns = {'Openness':'OPE', 'Conscientiousness':'CON', 'Extraversion': 'EXT', 'Agreeableness': 'AGR', 'Non-Neuroticism': 'NNEU'})
columns_to_round = ['OPE', 'CON', 'EXT', 'AGR', 'NNEU']
df[columns_to_round] = df[columns_to_round].apply(lambda x: [round(i, 3) for i in x])
df

[4]:
Path OPE CON EXT AGR NNEU Priority 1 Priority 2 Priority 3 Trait importance 1 Trait importance 2 Trait importance 3
Person ID
1 2d6btbaNdfo.000.mp4 0.619 0.661 0.478 0.654 0.601 Safe and reliable Practical and easy to use Economical/low cost Conscientiousness Agreeableness Openness
2 300gK3CnzW0.001.mp4 0.462 0.413 0.416 0.498 0.431 Classic car features Fashion and attention Luxury additions Agreeableness Conscientiousness Openness
3 300gK3CnzW0.003.mp4 0.468 0.449 0.372 0.510 0.454 Classic car features Fashion and attention Luxury additions Agreeableness Conscientiousness Openness
4 4vdJGgZpj4k.003.mp4 0.585 0.616 0.494 0.606 0.587 Safe and reliable Practical and easy to use Economical/low cost Conscientiousness Agreeableness Openness
5 be0DQawtVkE.002.mp4 0.681 0.566 0.554 0.647 0.642 Safe and reliable Practical and easy to use Economical/low cost Agreeableness Conscientiousness Openness
6 cLaZxEf1nE4.004.mp4 0.663 0.551 0.558 0.585 0.587 Safe and reliable Practical and easy to use Economical/low cost Conscientiousness Agreeableness Openness
7 g24JGYuT74A.004.mp4 0.590 0.399 0.410 0.532 0.507 Classic car features Recreation Luxury additions Agreeableness Conscientiousness Non-Neuroticism
8 JZNMxa3OKHY.000.mp4 0.606 0.524 0.531 0.594 0.580 Practical and easy to use Safe and reliable Economical/low cost Agreeableness Openness Non-Neuroticism
9 nvlqJbHk_Lc.003.mp4 0.511 0.465 0.391 0.444 0.439 Classic car features Fashion and attention Luxury additions Agreeableness Conscientiousness Openness
10 _plk5k7PBEg.003.mp4 0.648 0.610 0.525 0.614 0.606 Safe and reliable Practical and easy to use Economical/low cost Conscientiousness Agreeableness Openness

Predicting consumer preferences for industrial goods on the example of mobile device application categories

As an example, it is proposed to use the correlation coefficients between the personality traits and the mobile device application categories presented in the article:

  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.

[5]:

# Loading a dataframe with correlation coefficients
url = 'https://download.sberdisk.ru/download/file/478676690?token=7KcAxPqMpWiYQnx&filename=divice_characteristics.csv'
df_divice_characteristics = pd.read_csv(url)

df_divice_characteristics.index.name = 'ID'
df_divice_characteristics.index += 1
df_divice_characteristics.index = df_divice_characteristics.index.map(str)

df_divice_characteristics

[5]:
Trait Communication Game Action Game Board Game Casino Game Educational Game Simulation Game Trivia Entertainment Finance Health and Fitness Media and Video Music and Audio News and Magazines Personalisation Travel and Local Weather
ID
1 Openness 0.118 0.056 0.079 0.342 0.027 0.104 0.026 0.000 0.006 0.002 0.000 0.000 0.001 0.004 0.002 0.004
2 Conscientiousness 0.119 0.043 0.107 0.448 0.039 0.012 0.119 0.000 0.005 0.001 0.000 0.002 0.002 0.001 0.001 0.003
3 Extraversion 0.246 0.182 0.211 0.311 0.102 0.165 0.223 0.001 0.003 0.000 0.001 0.001 0.001 0.004 0.009 0.003
4 Agreeableness 0.218 0.104 0.164 0.284 0.165 0.122 0.162 0.000 0.003 0.001 0.000 0.002 0.002 0.001 0.004 0.003
5 Non-Neuroticism 0.046 0.047 0.125 0.515 0.272 0.179 0.214 0.002 0.030 0.001 0.000 0.005 0.003 0.008 0.004 0.007 
[6]:

_b5._priority_calculation(
    correlation_coefficients = df_divice_characteristics,
    col_name_ocean = 'Trait',
    threshold = 0.55,
    number_priority = 3,
    number_importance_traits = 3,
    out = True
)

_b5._save_logs(df = _b5.df_files_priority_, name = 'divice_characteristics_priorities_fi_en', out = True)

# Optional
df = _b5.df_files_priority_.rename(columns = {'Openness':'OPE', 'Conscientiousness':'CON', 'Extraversion': 'EXT', 'Agreeableness': 'AGR', 'Non-Neuroticism': 'NNEU'})
columns_to_round = ['OPE', 'CON', 'EXT', 'AGR', 'NNEU']
df[columns_to_round] = df[columns_to_round].apply(lambda x: [round(i, 3) for i in x])
df

[6]:
Path OPE CON EXT AGR NNEU Priority 1 Priority 2 Priority 3 Trait importance 1 Trait importance 2 Trait importance 3
Person ID
1 2d6btbaNdfo.000.mp4 0.619 0.661 0.478 0.654 0.601 Game Casino Game Educational Game Trivia Non-Neuroticism Conscientiousness Agreeableness
2 300gK3CnzW0.001.mp4 0.462 0.413 0.416 0.498 0.431 Media and Video Entertainment Health and Fitness Conscientiousness Agreeableness Extraversion
3 300gK3CnzW0.003.mp4 0.468 0.449 0.372 0.510 0.454 Media and Video Entertainment Health and Fitness Conscientiousness Agreeableness Extraversion
4 4vdJGgZpj4k.003.mp4 0.585 0.616 0.494 0.606 0.587 Game Casino Game Educational Game Trivia Non-Neuroticism Conscientiousness Agreeableness
5 be0DQawtVkE.002.mp4 0.681 0.566 0.554 0.647 0.642 Game Casino Communication Game Trivia Non-Neuroticism Extraversion Agreeableness
6 cLaZxEf1nE4.004.mp4 0.663 0.551 0.558 0.585 0.587 Game Casino Communication Game Trivia Non-Neuroticism Extraversion Agreeableness
7 g24JGYuT74A.004.mp4 0.590 0.399 0.410 0.532 0.507 Health and Fitness Media and Video Entertainment Openness Conscientiousness Agreeableness
8 JZNMxa3OKHY.000.mp4 0.606 0.524 0.531 0.594 0.580 Game Casino Game Educational Game Simulation Non-Neuroticism Agreeableness Openness
9 nvlqJbHk_Lc.003.mp4 0.511 0.465 0.391 0.444 0.439 Media and Video Entertainment Health and Fitness Agreeableness Conscientiousness Extraversion
10 _plk5k7PBEg.003.mp4 0.648 0.610 0.525 0.614 0.606 Game Casino Game Educational Game Trivia Non-Neuroticism Agreeableness Conscientiousness

Прогнозирование потребительских предпочтений по стилю одежды

В качестве примера предлагается использование коэффициентов корреляции между персональными качествами человека и стилем одежды, представленными в статье:

  1. Stolovy T. Styling the self: clothing practices, personality traits, and body image among Israeli women // Frontiers in psychology. - 2022. - vol. 12. - 719318.

[7]:

# Загрузка датафрейма с коэффициентами корреляции
url = 'https://download.sberdisk.ru/download/file/493644097?token=KGtSGMxjZtWXmBz&filename=df_%D1%81lothing_style_correlation.csv'
df_clothing_styles = pd.read_csv(url)

df_clothing_styles.index.name = 'ID'
df_clothing_styles.index += 1
df_clothing_styles.index = df_clothing_styles.index.map(str)

df_clothing_styles

[7]:
Trait Comfort Camouflage Assurance Fashion Individuality
ID
1 Openness 0.01 -0.24 0.31 0.07 0.31
2 Conscientiousness -0.03 -0.24 0.17 0.09 0.15
3 Extraversion -0.01 -0.19 0.30 0.13 0.14
4 Agreeableness 0.16 -0.16 0.15 -0.09 -0.05
5 Non-Neuroticism 0.03 -0.16 0.01 0.00 0.06 
[8]:

_b5._priority_calculation(
    correlation_coefficients = df_clothing_styles,
    col_name_ocean = 'Trait',
    threshold = 0.55,
    number_priority = 3,
    number_importance_traits = 3,
    out = True
)

_b5._save_logs(df = _b5.df_files_priority_, name = 'clothing_styles_priorities_fi_en', out = True)

# Опционно
df = _b5.df_files_priority_.rename(columns = {'Openness':'OPE', 'Conscientiousness':'CON', 'Extraversion': 'EXT', 'Agreeableness': 'AGR', 'Non-Neuroticism': 'NNEU'})
columns_to_round = ['OPE', 'CON', 'EXT', 'AGR', 'NNEU']
df[columns_to_round] = df[columns_to_round].apply(lambda x: [round(i, 3) for i in x])
df

[8]:
Path OPE CON EXT AGR NNEU Priority 1 Priority 2 Priority 3 Trait importance 1 Trait importance 2 Trait importance 3
Person ID
1 2d6btbaNdfo.000.mp4 0.619 0.661 0.478 0.654 0.601 Assurance Individuality Comfort Openness Conscientiousness Agreeableness
2 300gK3CnzW0.001.mp4 0.462 0.413 0.416 0.498 0.431 Camouflage Fashion Comfort Conscientiousness Openness Non-Neuroticism
3 300gK3CnzW0.003.mp4 0.468 0.449 0.372 0.510 0.454 Camouflage Fashion Comfort Conscientiousness Openness Non-Neuroticism
4 4vdJGgZpj4k.003.mp4 0.585 0.616 0.494 0.606 0.587 Assurance Individuality Comfort Openness Conscientiousness Agreeableness
5 be0DQawtVkE.002.mp4 0.681 0.566 0.554 0.647 0.642 Assurance Individuality Fashion Openness Extraversion Conscientiousness
6 cLaZxEf1nE4.004.mp4 0.663 0.551 0.558 0.585 0.587 Assurance Individuality Fashion Openness Extraversion Conscientiousness
7 g24JGYuT74A.004.mp4 0.590 0.399 0.410 0.532 0.507 Camouflage Individuality Fashion Agreeableness Openness Non-Neuroticism
8 JZNMxa3OKHY.000.mp4 0.606 0.524 0.531 0.594 0.580 Comfort Individuality Assurance Openness Agreeableness Non-Neuroticism
9 nvlqJbHk_Lc.003.mp4 0.511 0.465 0.391 0.444 0.439 Camouflage Comfort Fashion Conscientiousness Openness Non-Neuroticism
10 _plk5k7PBEg.003.mp4 0.648 0.610 0.525 0.614 0.606 Assurance Individuality Comfort Openness Conscientiousness Agreeableness

MuPTA (ru)

[9]:

import os
import pandas as pd

# Импорт модуля
from oceanai.modules.lab.build import Run

# Создание экземпляра класса
_b5 = Run()

corpus = 'mupta'
lang = 'ru'

# Настройка ядра
_b5.path_to_save_ = './models' # Директория для сохранения файла
_b5.chunk_size_ = 2000000      # Размер загрузки файла из сети за 1 шаг

# Формирование аудиомоделей
res_load_model_hc = _b5.load_audio_model_hc()
res_load_model_nn = _b5.load_audio_model_nn()

# Загрузка весов аудиомоделей
url = _b5.weights_for_big5_['audio'][corpus]['hc']['googledisk']
res_load_model_weights_hc = _b5.load_audio_model_weights_hc(url = url, force_reload = False)

url = _b5.weights_for_big5_['audio'][corpus]['nn']['googledisk']
res_load_model_weights_nn = _b5.load_audio_model_weights_nn(url = url, force_reload = False)

# Формирование видеомоделей
res_load_model_hc = _b5.load_video_model_hc(lang=lang)
res_load_model_deep_fe = _b5.load_video_model_deep_fe()
res_load_model_nn = _b5.load_video_model_nn()

# Загрузка весов видеомоделей
url = _b5.weights_for_big5_['video'][corpus]['hc']['googledisk']
res_load_model_weights_hc = _b5.load_video_model_weights_hc(url = url, force_reload = False)

url = _b5.weights_for_big5_['video'][corpus]['fe']['googledisk']
res_load_model_weights_deep_fe = _b5.load_video_model_weights_deep_fe(url = url, force_reload = False)

url = _b5.weights_for_big5_['video'][corpus]['nn']['googledisk']
res_load_model_weights_nn = _b5.load_video_model_weights_nn(url = url, force_reload = False)

# Загрузка словаря с экспертными признаками (текстовая модальность)
res_load_text_features = _b5.load_text_features()

# Формирование текстовых моделей
res_setup_translation_model = _b5.setup_translation_model() # только для русского языка
res_setup_translation_model = _b5.setup_bert_encoder(force_reload = False)
res_load_text_model_hc_fi = _b5.load_text_model_hc(corpus=corpus)
res_load_text_model_nn_fi = _b5.load_text_model_nn(corpus=corpus)

# Загрузка весов текстовых моделей
url = _b5.weights_for_big5_['text'][corpus]['hc']['googledisk']
res_load_text_model_weights_hc_fi = _b5.load_text_model_weights_hc(url = url, force_reload = False)

url = _b5.weights_for_big5_['text'][corpus]['nn']['googledisk']
res_load_text_model_weights_nn_fi = _b5.load_text_model_weights_nn(url = url, force_reload = False)

# Формирование модели для мультимодального объединения информации
res_load_avt_model_b5 = _b5.load_avt_model_b5()

# Загрузка весов модели для мультимодального объединения информации
url = _b5.weights_for_big5_['avt'][corpus]['b5']['googledisk']
res_load_avt_model_weights_b5 = _b5.load_avt_model_weights_b5(url = url, force_reload = False)

PATH_TO_DIR = './video_MuPTA/'
PATH_SAVE_VIDEO = './video_MuPTA/test/'

_b5.path_to_save_ = PATH_SAVE_VIDEO

# Загрузка 10 тестовых аудиовидеозаписей из корпуса MuPTA
# URL: https://hci.nw.ru/en/pages/mupta-corpus
domain = 'https://download.sberdisk.ru/download/file/'
tets_name_files = [
    '477995979?token=2cvyk7CS0mHx2MJ&filename=speaker_06_center_83.mov',
    '477995980?token=jGPtBPS69uzFU6Y&filename=speaker_01_center_83.mov',
    '477995967?token=zCaRbNB6ht5wMPq&filename=speaker_11_center_83.mov',
    '477995966?token=B1rbinDYRQKrI3T&filename=speaker_15_center_83.mov',
    '477995978?token=dEpVDtZg1EQiEQ9&filename=speaker_07_center_83.mov',
    '477995961?token=o1hVjw8G45q9L9Z&filename=speaker_19_center_83.mov',
    '477995964?token=5K220Aqf673VHPq&filename=speaker_23_center_83.mov',
    '477995965?token=v1LVD2KT1cU7Lpb&filename=speaker_24_center_83.mov',
    '477995962?token=tmaSGyyWLA6XCy9&filename=speaker_27_center_83.mov',
    '477995963?token=bTpo96qNDPcwGqb&filename=speaker_10_center_83.mov',
]

for curr_files in tets_name_files:
    _b5.download_file_from_url(url = domain + curr_files, out = True)

# Получение прогнозов
_b5.path_to_dataset_ = PATH_TO_DIR # Директория набора данных
_b5.ext_ = ['.mov'] # Расширения искомых файлов

# Полный путь к файлу с верными предсказаниями для подсчета точности
url_accuracy = _b5.true_traits_['mupta']['sberdisk']

_b5.get_avt_predictions(url_accuracy = url_accuracy, lang = lang)

[2024-10-10 18:20:29] Извлечение признаков (экспертных и нейросетевых) из текста …

[2024-10-10 18:20:30] Получение прогнозов и вычисление точности (мультимодальное объединение) …

    10 из 10 (100.0%) … GitHub:nbsphinx-math:OCEANAI\docs\source\user_guide:nbsphinx-math:notebooks\video_MuPTA:nbsphinx-math:test\speaker_27_center_83.mov …

Path Openness Conscientiousness Extraversion Agreeableness Non-Neuroticism
Person ID
1 speaker_01_center_83.mov 0.765745 0.696637 0.656309 0.75986 0.494141
2 speaker_06_center_83.mov 0.686514 0.659488 0.611838 0.749739 0.420672
3 speaker_07_center_83.mov 0.671993 0.661216 0.571759 0.704542 0.381026
4 speaker_10_center_83.mov 0.69828 0.59893 0.571893 0.674907 0.35082
5 speaker_11_center_83.mov 0.718329 0.598986 0.573518 0.73201 0.379845
6 speaker_15_center_83.mov 0.670932 0.671055 0.602337 0.708656 0.399527
7 speaker_19_center_83.mov 0.767261 0.658167 0.653367 0.801366 0.463443
8 speaker_23_center_83.mov 0.699837 0.684907 0.616671 0.806437 0.447853
9 speaker_24_center_83.mov 0.710566 0.66299 0.610562 0.711242 0.413696
10 speaker_27_center_83.mov 0.759404 0.712562 0.658357 0.830507 0.507612

[2024-10-10 18:20:30] Точность по отдельным персональным качествам личности человека …

Openness Conscientiousness Extraversion Agreeableness Non-Neuroticism Mean
Metrics
MAE 0.0706 0.0788 0.1328 0.1071 0.1002 0.0979
Accuracy 0.9294 0.9212 0.8672 0.8929 0.8998 0.9021

[2024-10-10 18:20:30] Средняя средних абсолютных ошибок: 0.0979, средняя точность: 0.9021 …

Лог файлы успешно сохранены …

— Время выполнения: 324.067 сек. —

[9]:

True

To predict consumer preferences for industrial goods, it is necessary to know the correlation coefficients that determine the relationship between personality traits and preferences in goods or services.

As an example, it is proposed to use the correlation coefficients between the personality traits and the characteristics of the cars presented in the article:

  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.

The user can set their own correlation coefficients.

Predicting consumer preferences for industrial goods on the example of car characteristics

[10]:

# Loading dataframe with correlation coefficients
url = 'https://download.sberdisk.ru/download/file/478675818?token=EjfLMqOeK8cfnOu&filename=auto_characteristics.csv'
df_correlation_coefficients = pd.read_csv(url)
df_correlation_coefficients = pd.DataFrame(
    df_correlation_coefficients.drop(['Style and performance', 'Safety and practicality'], axis = 1)
)
df_correlation_coefficients.index.name = 'ID'
df_correlation_coefficients.index += 1
df_correlation_coefficients.index = df_correlation_coefficients.index.map(str)

df_correlation_coefficients

[10]:
Trait Performance Classic car features Luxury additions Fashion and attention Recreation Technology Family friendly Safe and reliable Practical and easy to use Economical/low cost Basic features
ID
1 Openness 0.020000 -0.033333 -0.030000 -0.050000 0.033333 0.013333 -0.030000 0.136667 0.106667 0.093333 0.006667
2 Conscientiousness 0.013333 -0.193333 -0.063333 -0.096667 -0.096667 0.086667 -0.063333 0.280000 0.180000 0.130000 0.143333
3 Extraversion 0.133333 0.060000 0.106667 0.123333 0.126667 0.120000 0.090000 0.136667 0.043333 0.073333 0.050000
4 Agreeableness -0.036667 -0.193333 -0.133333 -0.133333 -0.090000 0.046667 -0.016667 0.240000 0.160000 0.120000 0.083333
5 Non-Neuroticism 0.016667 -0.006667 -0.010000 -0.006667 -0.033333 0.046667 -0.023333 0.093333 0.046667 0.046667 -0.040000 
[11]:

_b5._priority_calculation(
    correlation_coefficients = df_correlation_coefficients,
    col_name_ocean = 'Trait',
    threshold = 0.55,
    number_priority = 3,
    number_importance_traits = 3,
    out = False
)

_b5._save_logs(df = _b5.df_files_priority_, name = 'auto_characteristics_priorities_mupta_ru', out = True)

# Optional
df = _b5.df_files_priority_.rename(columns = {'Openness':'OPE', 'Conscientiousness':'CON', 'Extraversion': 'EXT', 'Agreeableness': 'AGR', 'Non-Neuroticism': 'NNEU'})
columns_to_round = ['OPE', 'CON', 'EXT', 'AGR', 'NNEU']
df[columns_to_round] = df[columns_to_round].apply(lambda x: [round(i, 3) for i in x])
df

[11]:
Path OPE CON EXT AGR NNEU Priority 1 Priority 2 Priority 3 Trait importance 1 Trait importance 2 Trait importance 3
Person ID
1 speaker_01_center_83.mov 0.766 0.697 0.656 0.760 0.494 Safe and reliable Practical and easy to use Economical/low cost Conscientiousness Agreeableness Openness
2 speaker_06_center_83.mov 0.687 0.659 0.612 0.750 0.421 Safe and reliable Practical and easy to use Economical/low cost Agreeableness Conscientiousness Openness
3 speaker_07_center_83.mov 0.672 0.661 0.572 0.705 0.381 Safe and reliable Practical and easy to use Economical/low cost Conscientiousness Agreeableness Openness
4 speaker_10_center_83.mov 0.698 0.599 0.572 0.675 0.351 Safe and reliable Practical and easy to use Economical/low cost Conscientiousness Agreeableness Openness
5 speaker_11_center_83.mov 0.718 0.599 0.574 0.732 0.380 Safe and reliable Practical and easy to use Economical/low cost Agreeableness Conscientiousness Openness
6 speaker_15_center_83.mov 0.671 0.671 0.602 0.709 0.400 Safe and reliable Practical and easy to use Economical/low cost Conscientiousness Agreeableness Openness
7 speaker_19_center_83.mov 0.767 0.658 0.653 0.801 0.463 Safe and reliable Practical and easy to use Economical/low cost Agreeableness Conscientiousness Openness
8 speaker_23_center_83.mov 0.700 0.685 0.617 0.806 0.448 Safe and reliable Practical and easy to use Economical/low cost Agreeableness Conscientiousness Openness
9 speaker_24_center_83.mov 0.711 0.663 0.611 0.711 0.414 Safe and reliable Practical and easy to use Economical/low cost Conscientiousness Agreeableness Openness
10 speaker_27_center_83.mov 0.759 0.713 0.658 0.831 0.508 Safe and reliable Practical and easy to use Economical/low cost Agreeableness Conscientiousness Openness

Predicting consumer preferences for industrial goods on the example of mobile device application categories

As an example, it is proposed to use the correlation coefficients between the personality traits and the mobile device application categories presented in the article:

  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.

[12]:

# Loading a dataframe with correlation coefficients
url = 'https://download.sberdisk.ru/download/file/478676690?token=7KcAxPqMpWiYQnx&filename=divice_characteristics.csv'
df_divice_characteristics = pd.read_csv(url)

df_divice_characteristics.index.name = 'ID'
df_divice_characteristics.index += 1
df_divice_characteristics.index = df_divice_characteristics.index.map(str)

df_divice_characteristics

[12]:
Trait Communication Game Action Game Board Game Casino Game Educational Game Simulation Game Trivia Entertainment Finance Health and Fitness Media and Video Music and Audio News and Magazines Personalisation Travel and Local Weather
ID
1 Openness 0.118 0.056 0.079 0.342 0.027 0.104 0.026 0.000 0.006 0.002 0.000 0.000 0.001 0.004 0.002 0.004
2 Conscientiousness 0.119 0.043 0.107 0.448 0.039 0.012 0.119 0.000 0.005 0.001 0.000 0.002 0.002 0.001 0.001 0.003
3 Extraversion 0.246 0.182 0.211 0.311 0.102 0.165 0.223 0.001 0.003 0.000 0.001 0.001 0.001 0.004 0.009 0.003
4 Agreeableness 0.218 0.104 0.164 0.284 0.165 0.122 0.162 0.000 0.003 0.001 0.000 0.002 0.002 0.001 0.004 0.003
5 Non-Neuroticism 0.046 0.047 0.125 0.515 0.272 0.179 0.214 0.002 0.030 0.001 0.000 0.005 0.003 0.008 0.004 0.007 
[13]:

_b5._priority_calculation(
    correlation_coefficients = df_divice_characteristics,
    col_name_ocean = 'Trait',
    threshold = 0.55,
    number_priority = 3,
    number_importance_traits = 3,
    out = True
)

_b5._save_logs(df = _b5.df_files_priority_, name = 'divice_characteristics_priorities_mupta_ru', out = True)

# Optional
df = _b5.df_files_priority_.rename(columns = {'Openness':'OPE', 'Conscientiousness':'CON', 'Extraversion': 'EXT', 'Agreeableness': 'AGR', 'Non-Neuroticism': 'NNEU'})
columns_to_round = ['OPE', 'CON', 'EXT', 'AGR', 'NNEU']
df[columns_to_round] = df[columns_to_round].apply(lambda x: [round(i, 3) for i in x])
df

[13]:
Path OPE CON EXT AGR NNEU Priority 1 Priority 2 Priority 3 Trait importance 1 Trait importance 2 Trait importance 3
Person ID
1 speaker_01_center_83.mov 0.766 0.697 0.656 0.760 0.494 Game Casino Communication Game Board Agreeableness Extraversion Conscientiousness
2 speaker_06_center_83.mov 0.687 0.659 0.612 0.750 0.421 Game Casino Communication Game Board Agreeableness Extraversion Conscientiousness
3 speaker_07_center_83.mov 0.672 0.661 0.572 0.705 0.381 Game Casino Communication Game Board Agreeableness Conscientiousness Extraversion
4 speaker_10_center_83.mov 0.698 0.599 0.572 0.675 0.351 Game Casino Communication Game Board Agreeableness Extraversion Conscientiousness
5 speaker_11_center_83.mov 0.718 0.599 0.574 0.732 0.380 Game Casino Communication Game Board Agreeableness Extraversion Conscientiousness
6 speaker_15_center_83.mov 0.671 0.671 0.602 0.709 0.400 Game Casino Communication Game Board Agreeableness Extraversion Conscientiousness
7 speaker_19_center_83.mov 0.767 0.658 0.653 0.801 0.463 Game Casino Communication Game Board Agreeableness Extraversion Conscientiousness
8 speaker_23_center_83.mov 0.700 0.685 0.617 0.806 0.448 Game Casino Communication Game Board Agreeableness Extraversion Conscientiousness
9 speaker_24_center_83.mov 0.711 0.663 0.611 0.711 0.414 Game Casino Communication Game Board Agreeableness Extraversion Conscientiousness
10 speaker_27_center_83.mov 0.759 0.713 0.658 0.831 0.508 Game Casino Communication Game Board Agreeableness Extraversion Conscientiousness

Прогнозирование потребительских предпочтений по стилю одежды

В качестве примера предлагается использование коэффициентов корреляции между персональными качествами человека и стилем одежды, представленными в статье:

  1. Stolovy T. Styling the self: clothing practices, personality traits, and body image among Israeli women // Frontiers in psychology. - 2022. - vol. 12. - 719318.

[14]:

# Загрузка датафрейма с коэффициентами корреляции
url = 'https://download.sberdisk.ru/download/file/493644097?token=KGtSGMxjZtWXmBz&filename=df_%D1%81lothing_style_correlation.csv'
df_clothing_styles = pd.read_csv(url)

df_clothing_styles.index.name = 'ID'
df_clothing_styles.index += 1
df_clothing_styles.index = df_clothing_styles.index.map(str)

df_clothing_styles

[14]:
Trait Comfort Camouflage Assurance Fashion Individuality
ID
1 Openness 0.01 -0.24 0.31 0.07 0.31
2 Conscientiousness -0.03 -0.24 0.17 0.09 0.15
3 Extraversion -0.01 -0.19 0.30 0.13 0.14
4 Agreeableness 0.16 -0.16 0.15 -0.09 -0.05
5 Non-Neuroticism 0.03 -0.16 0.01 0.00 0.06 
[15]:

_b5._priority_calculation(
    correlation_coefficients = df_clothing_styles,
    col_name_ocean = 'Trait',
    threshold = 0.55,
    number_priority = 3,
    number_importance_traits = 3,
    out = True
)

_b5._save_logs(df = _b5.df_files_priority_, name = 'clothing_styles_priorities_mupta_ru', out = True)

# Опционно
df = _b5.df_files_priority_.rename(columns = {'Openness':'OPE', 'Conscientiousness':'CON', 'Extraversion': 'EXT', 'Agreeableness': 'AGR', 'Non-Neuroticism': 'NNEU'})
columns_to_round = ['OPE', 'CON', 'EXT', 'AGR', 'NNEU']
df[columns_to_round] = df[columns_to_round].apply(lambda x: [round(i, 3) for i in x])
df

[15]:
Path OPE CON EXT AGR NNEU Priority 1 Priority 2 Priority 3 Trait importance 1 Trait importance 2 Trait importance 3
Person ID
1 speaker_01_center_83.mov 0.766 0.697 0.656 0.760 0.494 Assurance Individuality Fashion Openness Extraversion Conscientiousness
2 speaker_06_center_83.mov 0.687 0.659 0.612 0.750 0.421 Assurance Individuality Fashion Openness Extraversion Conscientiousness
3 speaker_07_center_83.mov 0.672 0.661 0.572 0.705 0.381 Assurance Individuality Fashion Openness Extraversion Conscientiousness
4 speaker_10_center_83.mov 0.698 0.599 0.572 0.675 0.351 Assurance Individuality Fashion Openness Extraversion Conscientiousness
5 speaker_11_center_83.mov 0.718 0.599 0.574 0.732 0.380 Assurance Individuality Fashion Openness Extraversion Conscientiousness
6 speaker_15_center_83.mov 0.671 0.671 0.602 0.709 0.400 Assurance Individuality Fashion Openness Extraversion Conscientiousness
7 speaker_19_center_83.mov 0.767 0.658 0.653 0.801 0.463 Assurance Individuality Fashion Openness Extraversion Conscientiousness
8 speaker_23_center_83.mov 0.700 0.685 0.617 0.806 0.448 Assurance Individuality Fashion Openness Extraversion Conscientiousness
9 speaker_24_center_83.mov 0.711 0.663 0.611 0.711 0.414 Assurance Individuality Fashion Openness Extraversion Conscientiousness
10 speaker_27_center_83.mov 0.759 0.713 0.658 0.831 0.508 Assurance Individuality Fashion Openness Extraversion Conscientiousness

MuPTA (en)

[16]:

import os
import pandas as pd

# Импорт модуля
from oceanai.modules.lab.build import Run

# Создание экземпляра класса
_b5 = Run()

corpus = 'fi'
lang = 'en'

# Настройка ядра
_b5.path_to_save_ = './models' # Директория для сохранения файла
_b5.chunk_size_ = 2000000      # Размер загрузки файла из сети за 1 шаг

# Формирование аудиомоделей
res_load_model_hc = _b5.load_audio_model_hc()
res_load_model_nn = _b5.load_audio_model_nn()

# Загрузка весов аудиомоделей
url = _b5.weights_for_big5_['audio'][corpus]['hc']['googledisk']
res_load_model_weights_hc = _b5.load_audio_model_weights_hc(url = url, force_reload = False)

url = _b5.weights_for_big5_['audio'][corpus]['nn']['googledisk']
res_load_model_weights_nn = _b5.load_audio_model_weights_nn(url = url, force_reload = False)

# Формирование видеомоделей
res_load_model_hc = _b5.load_video_model_hc(lang=lang)
res_load_model_deep_fe = _b5.load_video_model_deep_fe()
res_load_model_nn = _b5.load_video_model_nn()

# Загрузка весов видеомоделей
url = _b5.weights_for_big5_['video'][corpus]['hc']['googledisk']
res_load_model_weights_hc = _b5.load_video_model_weights_hc(url = url, force_reload = False)

url = _b5.weights_for_big5_['video'][corpus]['fe']['googledisk']
res_load_model_weights_deep_fe = _b5.load_video_model_weights_deep_fe(url = url, force_reload = False)

url = _b5.weights_for_big5_['video'][corpus]['nn']['googledisk']
res_load_model_weights_nn = _b5.load_video_model_weights_nn(url = url, force_reload = False)

# Загрузка словаря с экспертными признаками (текстовая модальность)
res_load_text_features = _b5.load_text_features()

# Формирование текстовых моделей
res_setup_translation_model = _b5.setup_translation_model() # только для русского языка
res_setup_translation_model = _b5.setup_bert_encoder(force_reload = False)
res_load_text_model_hc_fi = _b5.load_text_model_hc(corpus=corpus)
res_load_text_model_nn_fi = _b5.load_text_model_nn(corpus=corpus)

# Загрузка весов текстовых моделей
url = _b5.weights_for_big5_['text'][corpus]['hc']['googledisk']
res_load_text_model_weights_hc_fi = _b5.load_text_model_weights_hc(url = url, force_reload = False)

url = _b5.weights_for_big5_['text'][corpus]['nn']['googledisk']
res_load_text_model_weights_nn_fi = _b5.load_text_model_weights_nn(url = url, force_reload = False)

# Формирование модели для мультимодального объединения информации
res_load_avt_model_b5 = _b5.load_avt_model_b5()

# Загрузка весов модели для мультимодального объединения информации
url = _b5.weights_for_big5_['avt'][corpus]['b5']['googledisk']
res_load_avt_model_weights_b5 = _b5.load_avt_model_weights_b5(url = url, force_reload = False)

PATH_TO_DIR = './video_MuPTA/'
PATH_SAVE_VIDEO = './video_MuPTA/test/'

_b5.path_to_save_ = PATH_SAVE_VIDEO

# Загрузка 10 тестовых аудиовидеозаписей из корпуса MuPTA
# URL: https://hci.nw.ru/en/pages/mupta-corpus
domain = 'https://download.sberdisk.ru/download/file/'
tets_name_files = [
    '477995979?token=2cvyk7CS0mHx2MJ&filename=speaker_06_center_83.mov',
    '477995980?token=jGPtBPS69uzFU6Y&filename=speaker_01_center_83.mov',
    '477995967?token=zCaRbNB6ht5wMPq&filename=speaker_11_center_83.mov',
    '477995966?token=B1rbinDYRQKrI3T&filename=speaker_15_center_83.mov',
    '477995978?token=dEpVDtZg1EQiEQ9&filename=speaker_07_center_83.mov',
    '477995961?token=o1hVjw8G45q9L9Z&filename=speaker_19_center_83.mov',
    '477995964?token=5K220Aqf673VHPq&filename=speaker_23_center_83.mov',
    '477995965?token=v1LVD2KT1cU7Lpb&filename=speaker_24_center_83.mov',
    '477995962?token=tmaSGyyWLA6XCy9&filename=speaker_27_center_83.mov',
    '477995963?token=bTpo96qNDPcwGqb&filename=speaker_10_center_83.mov',
]

for curr_files in tets_name_files:
    _b5.download_file_from_url(url = domain + curr_files, out = True)

# Получение прогнозов
_b5.path_to_dataset_ = PATH_TO_DIR # Директория набора данных
_b5.ext_ = ['.mov'] # Расширения искомых файлов

# Полный путь к файлу с верными предсказаниями для подсчета точности
url_accuracy = _b5.true_traits_['mupta']['sberdisk']

_b5.get_avt_predictions(url_accuracy = url_accuracy, lang = lang)

[2024-10-10 18:29:55] Извлечение признаков (экспертных и нейросетевых) из текста …

[2024-10-10 18:29:56] Получение прогнозов и вычисление точности (мультимодальное объединение) …

    10 из 10 (100.0%) … GitHub:nbsphinx-math:OCEANAI\docs\source\user_guide:nbsphinx-math:notebooks\video_MuPTA:nbsphinx-math:test\speaker_27_center_83.mov …

Path Openness Conscientiousness Extraversion Agreeableness Non-Neuroticism
Person ID
1 speaker_01_center_83.mov 0.59561 0.542967 0.440668 0.589769 0.515306
2 speaker_06_center_83.mov 0.661347 0.673973 0.603208 0.64543 0.6431
3 speaker_07_center_83.mov 0.439868 0.465049 0.284547 0.422551 0.396058
4 speaker_10_center_83.mov 0.47715 0.502563 0.373686 0.441372 0.424637
5 speaker_11_center_83.mov 0.403292 0.344359 0.317304 0.422228 0.384346
6 speaker_15_center_83.mov 0.581837 0.562177 0.504623 0.602169 0.522254
7 speaker_19_center_83.mov 0.510444 0.448468 0.425599 0.451861 0.447891
8 speaker_23_center_83.mov 0.500526 0.541376 0.308529 0.441178 0.452412
9 speaker_24_center_83.mov 0.427677 0.511355 0.301078 0.434281 0.442301
10 speaker_27_center_83.mov 0.566414 0.659169 0.434059 0.59122 0.579172

[2024-10-10 18:29:56] Точность по отдельным персональным качествам личности человека …

Openness Conscientiousness Extraversion Agreeableness Non-Neuroticism Mean
Metrics
MAE 0.1632 0.1621 0.176 0.2589 0.1122 0.1745
Accuracy 0.8368 0.8379 0.824 0.7411 0.8878 0.8255

[2024-10-10 18:29:56] Средняя средних абсолютных ошибок: 0.1745, средняя точность: 0.8255 …

Лог файлы успешно сохранены …

— Время выполнения: 320.737 сек. —

[16]:

True

To predict consumer preferences for industrial goods, it is necessary to know the correlation coefficients that determine the relationship between personality traits and preferences in goods or services.

As an example, it is proposed to use the correlation coefficients between the personality traits and the characteristics of the cars presented in the article:

  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.

The user can set their own correlation coefficients.

Predicting consumer preferences for industrial goods on the example of car characteristics

[17]:

# Loading dataframe with correlation coefficients
url = 'https://download.sberdisk.ru/download/file/478675818?token=EjfLMqOeK8cfnOu&filename=auto_characteristics.csv'
df_correlation_coefficients = pd.read_csv(url)
df_correlation_coefficients = pd.DataFrame(
    df_correlation_coefficients.drop(['Style and performance', 'Safety and practicality'], axis = 1)
)
df_correlation_coefficients.index.name = 'ID'
df_correlation_coefficients.index += 1
df_correlation_coefficients.index = df_correlation_coefficients.index.map(str)

df_correlation_coefficients

[17]:
Trait Performance Classic car features Luxury additions Fashion and attention Recreation Technology Family friendly Safe and reliable Practical and easy to use Economical/low cost Basic features
ID
1 Openness 0.020000 -0.033333 -0.030000 -0.050000 0.033333 0.013333 -0.030000 0.136667 0.106667 0.093333 0.006667
2 Conscientiousness 0.013333 -0.193333 -0.063333 -0.096667 -0.096667 0.086667 -0.063333 0.280000 0.180000 0.130000 0.143333
3 Extraversion 0.133333 0.060000 0.106667 0.123333 0.126667 0.120000 0.090000 0.136667 0.043333 0.073333 0.050000
4 Agreeableness -0.036667 -0.193333 -0.133333 -0.133333 -0.090000 0.046667 -0.016667 0.240000 0.160000 0.120000 0.083333
5 Non-Neuroticism 0.016667 -0.006667 -0.010000 -0.006667 -0.033333 0.046667 -0.023333 0.093333 0.046667 0.046667 -0.040000 
[18]:

_b5._priority_calculation(
    correlation_coefficients = df_correlation_coefficients,
    col_name_ocean = 'Trait',
    threshold = 0.55,
    number_priority = 3,
    number_importance_traits = 3,
    out = False
)

_b5._save_logs(df = _b5.df_files_priority_, name = 'auto_characteristics_priorities_mupta_en', out = True)

# Optional
df = _b5.df_files_priority_.rename(columns = {'Openness':'OPE', 'Conscientiousness':'CON', 'Extraversion': 'EXT', 'Agreeableness': 'AGR', 'Non-Neuroticism': 'NNEU'})
columns_to_round = ['OPE', 'CON', 'EXT', 'AGR', 'NNEU']
df[columns_to_round] = df[columns_to_round].apply(lambda x: [round(i, 3) for i in x])
df

[18]:
Path OPE CON EXT AGR NNEU Priority 1 Priority 2 Priority 3 Trait importance 1 Trait importance 2 Trait importance 3
Person ID
1 speaker_01_center_83.mov 0.596 0.543 0.441 0.590 0.515 Practical and easy to use Economical/low cost Recreation Openness Agreeableness Non-Neuroticism
2 speaker_06_center_83.mov 0.661 0.674 0.603 0.645 0.643 Safe and reliable Practical and easy to use Economical/low cost Conscientiousness Agreeableness Openness
3 speaker_07_center_83.mov 0.440 0.465 0.285 0.423 0.396 Classic car features Fashion and attention Luxury additions Agreeableness Conscientiousness Openness
4 speaker_10_center_83.mov 0.477 0.503 0.374 0.441 0.425 Classic car features Fashion and attention Luxury additions Agreeableness Conscientiousness Openness
5 speaker_11_center_83.mov 0.403 0.344 0.317 0.422 0.384 Classic car features Fashion and attention Luxury additions Agreeableness Conscientiousness Openness
6 speaker_15_center_83.mov 0.582 0.562 0.505 0.602 0.522 Safe and reliable Practical and easy to use Economical/low cost Conscientiousness Agreeableness Openness
7 speaker_19_center_83.mov 0.510 0.448 0.426 0.452 0.448 Classic car features Fashion and attention Luxury additions Agreeableness Conscientiousness Openness
8 speaker_23_center_83.mov 0.501 0.541 0.309 0.441 0.452 Classic car features Fashion and attention Luxury additions Agreeableness Conscientiousness Openness
9 speaker_24_center_83.mov 0.428 0.511 0.301 0.434 0.442 Classic car features Fashion and attention Luxury additions Agreeableness Conscientiousness Openness
10 speaker_27_center_83.mov 0.566 0.659 0.434 0.591 0.579 Safe and reliable Practical and easy to use Economical/low cost Conscientiousness Agreeableness Openness

Predicting consumer preferences for industrial goods on the example of mobile device application categories

As an example, it is proposed to use the correlation coefficients between the personality traits and the mobile device application categories presented in the article:

  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.

[19]:

# Loading a dataframe with correlation coefficients
url = 'https://download.sberdisk.ru/download/file/478676690?token=7KcAxPqMpWiYQnx&filename=divice_characteristics.csv'
df_divice_characteristics = pd.read_csv(url)

df_divice_characteristics.index.name = 'ID'
df_divice_characteristics.index += 1
df_divice_characteristics.index = df_divice_characteristics.index.map(str)

df_divice_characteristics

[19]:
Trait Communication Game Action Game Board Game Casino Game Educational Game Simulation Game Trivia Entertainment Finance Health and Fitness Media and Video Music and Audio News and Magazines Personalisation Travel and Local Weather
ID
1 Openness 0.118 0.056 0.079 0.342 0.027 0.104 0.026 0.000 0.006 0.002 0.000 0.000 0.001 0.004 0.002 0.004
2 Conscientiousness 0.119 0.043 0.107 0.448 0.039 0.012 0.119 0.000 0.005 0.001 0.000 0.002 0.002 0.001 0.001 0.003
3 Extraversion 0.246 0.182 0.211 0.311 0.102 0.165 0.223 0.001 0.003 0.000 0.001 0.001 0.001 0.004 0.009 0.003
4 Agreeableness 0.218 0.104 0.164 0.284 0.165 0.122 0.162 0.000 0.003 0.001 0.000 0.002 0.002 0.001 0.004 0.003
5 Non-Neuroticism 0.046 0.047 0.125 0.515 0.272 0.179 0.214 0.002 0.030 0.001 0.000 0.005 0.003 0.008 0.004 0.007 
[20]:

_b5._priority_calculation(
    correlation_coefficients = df_divice_characteristics,
    col_name_ocean = 'Trait',
    threshold = 0.55,
    number_priority = 3,
    number_importance_traits = 3,
    out = True
)

_b5._save_logs(df = _b5.df_files_priority_, name = 'divice_characteristics_priorities_mupta_en', out = True)

# Optional
df = _b5.df_files_priority_.rename(columns = {'Openness':'OPE', 'Conscientiousness':'CON', 'Extraversion': 'EXT', 'Agreeableness': 'AGR', 'Non-Neuroticism': 'NNEU'})
columns_to_round = ['OPE', 'CON', 'EXT', 'AGR', 'NNEU']
df[columns_to_round] = df[columns_to_round].apply(lambda x: [round(i, 3) for i in x])
df

[20]:
Path OPE CON EXT AGR NNEU Priority 1 Priority 2 Priority 3 Trait importance 1 Trait importance 2 Trait importance 3
Person ID
1 speaker_01_center_83.mov 0.596 0.543 0.441 0.590 0.515 Communication Health and Fitness Media and Video Agreeableness Openness Non-Neuroticism
2 speaker_06_center_83.mov 0.661 0.674 0.603 0.645 0.643 Game Casino Communication Game Trivia Non-Neuroticism Extraversion Conscientiousness
3 speaker_07_center_83.mov 0.440 0.465 0.285 0.423 0.396 Media and Video Entertainment Health and Fitness Agreeableness Conscientiousness Extraversion
4 speaker_10_center_83.mov 0.477 0.503 0.374 0.441 0.425 Media and Video Entertainment Health and Fitness Agreeableness Conscientiousness Extraversion
5 speaker_11_center_83.mov 0.403 0.344 0.317 0.422 0.384 Media and Video Entertainment Health and Fitness Conscientiousness Agreeableness Extraversion
6 speaker_15_center_83.mov 0.582 0.562 0.505 0.602 0.522 Game Casino Communication Game Board Agreeableness Conscientiousness Openness
7 speaker_19_center_83.mov 0.510 0.448 0.426 0.452 0.448 Media and Video Entertainment Health and Fitness Conscientiousness Agreeableness Extraversion
8 speaker_23_center_83.mov 0.501 0.541 0.309 0.441 0.452 Media and Video Entertainment Health and Fitness Agreeableness Conscientiousness Extraversion
9 speaker_24_center_83.mov 0.428 0.511 0.301 0.434 0.442 Media and Video Entertainment Health and Fitness Agreeableness Conscientiousness Extraversion
10 speaker_27_center_83.mov 0.566 0.659 0.434 0.591 0.579 Game Casino Game Educational Game Trivia Non-Neuroticism Conscientiousness Agreeableness

Прогнозирование потребительских предпочтений по стилю одежды

В качестве примера предлагается использование коэффициентов корреляции между персональными качествами человека и стилем одежды, представленными в статье:

  1. Stolovy T. Styling the self: clothing practices, personality traits, and body image among Israeli women // Frontiers in psychology. - 2022. - vol. 12. - 719318.

[21]:

# Загрузка датафрейма с коэффициентами корреляции
url = 'https://download.sberdisk.ru/download/file/493644097?token=KGtSGMxjZtWXmBz&filename=df_%D1%81lothing_style_correlation.csv'
df_clothing_styles = pd.read_csv(url)

df_clothing_styles.index.name = 'ID'
df_clothing_styles.index += 1
df_clothing_styles.index = df_clothing_styles.index.map(str)

df_clothing_styles

[21]:
Trait Comfort Camouflage Assurance Fashion Individuality
ID
1 Openness 0.01 -0.24 0.31 0.07 0.31
2 Conscientiousness -0.03 -0.24 0.17 0.09 0.15
3 Extraversion -0.01 -0.19 0.30 0.13 0.14
4 Agreeableness 0.16 -0.16 0.15 -0.09 -0.05
5 Non-Neuroticism 0.03 -0.16 0.01 0.00 0.06 
[22]:

_b5._priority_calculation(
    correlation_coefficients = df_clothing_styles,
    col_name_ocean = 'Trait',
    threshold = 0.55,
    number_priority = 3,
    number_importance_traits = 3,
    out = True
)

_b5._save_logs(df = _b5.df_files_priority_, name = 'clothing_styles_priorities_mupta_en', out = True)

# Опционно
df = _b5.df_files_priority_.rename(columns = {'Openness':'OPE', 'Conscientiousness':'CON', 'Extraversion': 'EXT', 'Agreeableness': 'AGR', 'Non-Neuroticism': 'NNEU'})
columns_to_round = ['OPE', 'CON', 'EXT', 'AGR', 'NNEU']
df[columns_to_round] = df[columns_to_round].apply(lambda x: [round(i, 3) for i in x])
df

[22]:
Path OPE CON EXT AGR NNEU Priority 1 Priority 2 Priority 3 Trait importance 1 Trait importance 2 Trait importance 3
Person ID
1 speaker_01_center_83.mov 0.596 0.543 0.441 0.590 0.515 Comfort Camouflage Assurance Agreeableness Non-Neuroticism Conscientiousness
2 speaker_06_center_83.mov 0.661 0.674 0.603 0.645 0.643 Assurance Individuality Fashion Openness Extraversion Conscientiousness
3 speaker_07_center_83.mov 0.440 0.465 0.285 0.423 0.396 Camouflage Comfort Fashion Conscientiousness Openness Non-Neuroticism
4 speaker_10_center_83.mov 0.477 0.503 0.374 0.441 0.425 Camouflage Comfort Fashion Conscientiousness Openness Non-Neuroticism
5 speaker_11_center_83.mov 0.403 0.344 0.317 0.422 0.384 Camouflage Fashion Comfort Openness Conscientiousness Non-Neuroticism
6 speaker_15_center_83.mov 0.582 0.562 0.505 0.602 0.522 Assurance Individuality Comfort Openness Conscientiousness Agreeableness
7 speaker_19_center_83.mov 0.510 0.448 0.426 0.452 0.448 Camouflage Comfort Fashion Openness Conscientiousness Non-Neuroticism
8 speaker_23_center_83.mov 0.501 0.541 0.309 0.441 0.452 Camouflage Comfort Fashion Conscientiousness Openness Non-Neuroticism
9 speaker_24_center_83.mov 0.428 0.511 0.301 0.434 0.442 Camouflage Comfort Fashion Conscientiousness Openness Non-Neuroticism
10 speaker_27_center_83.mov 0.566 0.659 0.434 0.591 0.579 Assurance Individuality Comfort Openness Conscientiousness Agreeableness