Решение практической задачи 2

Задача: прогнозирование потребительских предпочтений на промышленные товары

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.

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.

c19d9a4612694238b82ed31c272bd21e

5fbfe4111a254c34bd71ce3525e7c225

`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']['sberdisk']
res_load_model_weights_hc = _b5.load_audio_model_weights_hc(url = url)

url = _b5.weights_for_big5_['audio'][corpus]['nn']['sberdisk']
res_load_model_weights_nn = _b5.load_audio_model_weights_nn(url = url)

# 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']['sberdisk']
res_load_model_weights_hc = _b5.load_video_model_weights_hc(url = url)

url = _b5.weights_for_big5_['video'][corpus]['fe']['sberdisk']
res_load_model_weights_deep_fe = _b5.load_video_model_weights_deep_fe(url = url)

url = _b5.weights_for_big5_['video'][corpus]['nn']['sberdisk']
res_load_model_weights_nn = _b5.load_video_model_weights_nn(url = url)

# 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']['sberdisk']
res_load_text_model_weights_hc_fi = _b5.load_text_model_weights_hc(url = url)

url = _b5.weights_for_big5_['text'][corpus]['nn']['sberdisk']
res_load_text_model_weights_nn_fi = _b5.load_text_model_weights_nn(url = url)

# 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']['sberdisk']
res_load_avt_model_weights_b5 = _b5.load_avt_model_weights_b5(url = url)

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]['sberdisk']

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

[2023-12-16 19:05:15] Извлечение признаков (экспертных и нейросетевых) из текста …

[2023-12-16 19:05:17] 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	0.581159	0.628822	0.466609	0.622129	0.553832
2	300gK3CnzW0.001.mp4	0.463991	0.418851	0.41301	0.493329	0.423093
3	300gK3CnzW0.003.mp4	0.454281	0.415049	0.39189	0.485114	0.420741
4	4vdJGgZpj4k.003.mp4	0.588461	0.643233	0.530789	0.603038	0.593398
5	be0DQawtVkE.002.mp4	0.633433	0.533295	0.523742	0.608591	0.588456
6	cLaZxEf1nE4.004.mp4	0.636944	0.542386	0.558461	0.570975	0.558983
7	g24JGYuT74A.004.mp4	0.531518	0.376987	0.393309	0.4904	0.447881
8	JZNMxa3OKHY.000.mp4	0.610342	0.541418	0.563163	0.595013	0.569461
9	nvlqJbHk_Lc.003.mp4	0.495809	0.458526	0.414436	0.469152	0.435461
10	_plk5k7PBEg.003.mp4	0.60707	0.591893	0.520662	0.603938	0.565726

[2023-12-16 19:05:17] Точность по отдельным персональным качествам личности человека …

	Openness	Conscientiousness	Extraversion	Agreeableness	Non-Neuroticism	Mean
Metrics
MAE	0.0589	0.0612	0.0864	0.0697	0.0582	0.0669
Accuracy	0.9411	0.9388	0.9136	0.9303	0.9418	0.9331

[2023-12-16 19:05:17] Средняя средних абсолютных ошибок: 0.0669, средняя точность: 0.9331 …

Лог файлы успешно сохранены …

— Время выполнения: 64.147 сек. —

[2]:

True

Для прогнозирования потребительских предпочтений в промышленных товарах необходимо знать коэффициенты корреляции, определяющие взаимосвязь между персональными качествами личности человека и предпочтениями в товарах или услугах.

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

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.

Пользователь может установить свои коэффициенты корреляции.

Прогнозирование потребительских предпочтений на характеристики атомобиля

[3]:

# Загрузка датафрейма с коэффициентами корреляции
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)

# 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

[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	0.581	0.629	0.467	0.622	0.554	Safe and reliable	Practical and easy to use	Economical/low cost	Conscientiousness	Agreeableness	Openness
2	300gK3CnzW0.001.mp4	0.464	0.419	0.413	0.493	0.423	Classic car features	Fashion and attention	Luxury additions	Agreeableness	Conscientiousness	Openness
3	300gK3CnzW0.003.mp4	0.454	0.415	0.392	0.485	0.421	Classic car features	Fashion and attention	Luxury additions	Agreeableness	Conscientiousness	Openness
4	4vdJGgZpj4k.003.mp4	0.588	0.643	0.531	0.603	0.593	Safe and reliable	Practical and easy to use	Economical/low cost	Conscientiousness	Agreeableness	Openness
5	be0DQawtVkE.002.mp4	0.633	0.533	0.524	0.609	0.588	Practical and easy to use	Safe and reliable	Economical/low cost	Agreeableness	Openness	Non-Neuroticism
6	cLaZxEf1nE4.004.mp4	0.637	0.542	0.558	0.571	0.559	Safe and reliable	Economical/low cost	Practical and easy to use	Agreeableness	Openness	Extraversion
7	g24JGYuT74A.004.mp4	0.532	0.377	0.393	0.490	0.448	Classic car features	Fashion and attention	Luxury additions	Agreeableness	Conscientiousness	Openness
8	JZNMxa3OKHY.000.mp4	0.610	0.541	0.563	0.595	0.569	Safe and reliable	Economical/low cost	Practical and easy to use	Agreeableness	Openness	Extraversion
9	nvlqJbHk_Lc.003.mp4	0.496	0.459	0.414	0.469	0.435	Classic car features	Fashion and attention	Luxury additions	Agreeableness	Conscientiousness	Openness
10	_plk5k7PBEg.003.mp4	0.607	0.592	0.521	0.604	0.566	Safe and reliable	Practical and easy to use	Economical/low cost	Conscientiousness	Agreeableness	Openness

Прогнозирование потребительских предпочтений на характеристики мобильного устройства

В качестве примера предлагается использование коэффициентов корреляции между персональными качествами человека и характеристиками мобильного устройства, представленными в статье:

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]:

# Загрузка датафрейма с коэффициентами корреляции
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)

# 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

[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	0.581	0.629	0.467	0.622	0.554	Game Casino	Game Educational	Game Trivia	Non-Neuroticism	Conscientiousness	Agreeableness
2	300gK3CnzW0.001.mp4	0.464	0.419	0.413	0.493	0.423	Media and Video	Entertainment	Health and Fitness	Conscientiousness	Agreeableness	Extraversion
3	300gK3CnzW0.003.mp4	0.454	0.415	0.392	0.485	0.421	Media and Video	Entertainment	Health and Fitness	Conscientiousness	Agreeableness	Extraversion
4	4vdJGgZpj4k.003.mp4	0.588	0.643	0.531	0.603	0.593	Game Casino	Game Educational	Game Trivia	Non-Neuroticism	Conscientiousness	Agreeableness
5	be0DQawtVkE.002.mp4	0.633	0.533	0.524	0.609	0.588	Game Casino	Game Educational	Game Simulation	Non-Neuroticism	Agreeableness	Openness
6	cLaZxEf1nE4.004.mp4	0.637	0.542	0.558	0.571	0.559	Game Casino	Game Simulation	Game Educational	Non-Neuroticism	Agreeableness	Extraversion
7	g24JGYuT74A.004.mp4	0.532	0.377	0.393	0.490	0.448	Media and Video	Entertainment	Health and Fitness	Conscientiousness	Agreeableness	Extraversion
8	JZNMxa3OKHY.000.mp4	0.610	0.541	0.563	0.595	0.569	Game Casino	Game Simulation	Game Educational	Non-Neuroticism	Agreeableness	Extraversion
9	nvlqJbHk_Lc.003.mp4	0.496	0.459	0.414	0.469	0.435	Media and Video	Entertainment	Health and Fitness	Conscientiousness	Agreeableness	Extraversion
10	_plk5k7PBEg.003.mp4	0.607	0.592	0.521	0.604	0.566	Game Casino	Game Educational	Game Trivia	Non-Neuroticism	Agreeableness	Conscientiousness

`MuPTA` (ru)

[7]:

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']['sberdisk']
res_load_model_weights_hc = _b5.load_audio_model_weights_hc(url = url)

url = _b5.weights_for_big5_['audio'][corpus]['nn']['sberdisk']
res_load_model_weights_nn = _b5.load_audio_model_weights_nn(url = url)

# 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']['sberdisk']
res_load_model_weights_hc = _b5.load_video_model_weights_hc(url = url)

url = _b5.weights_for_big5_['video'][corpus]['fe']['sberdisk']
res_load_model_weights_deep_fe = _b5.load_video_model_weights_deep_fe(url = url)

url = _b5.weights_for_big5_['video'][corpus]['nn']['sberdisk']
res_load_model_weights_nn = _b5.load_video_model_weights_nn(url = url)

# 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']['sberdisk']
res_load_text_model_weights_hc_fi = _b5.load_text_model_weights_hc(url = url)

url = _b5.weights_for_big5_['text'][corpus]['nn']['sberdisk']
res_load_text_model_weights_nn_fi = _b5.load_text_model_weights_nn(url = url)

# 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']['sberdisk']
res_load_avt_model_weights_b5 = _b5.load_avt_model_weights_b5(url = url)

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)

[2023-12-16 19:13:25] Извлечение признаков (экспертных и нейросетевых) из текста …

[2023-12-16 19:13:30] 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	0.758137	0.693356	0.650108	0.744589	0.488671
2	speaker_06_center_83.mov	0.681602	0.654339	0.607156	0.731282	0.417908
3	speaker_07_center_83.mov	0.666104	0.656836	0.567863	0.685067	0.378102
4	speaker_10_center_83.mov	0.694171	0.596195	0.571414	0.66223	0.348639
5	speaker_11_center_83.mov	0.712885	0.594764	0.571709	0.716696	0.37802
6	speaker_15_center_83.mov	0.664158	0.670411	0.60421	0.696056	0.399842
7	speaker_19_center_83.mov	0.761213	0.652635	0.651028	0.788677	0.459676
8	speaker_23_center_83.mov	0.692788	0.68324	0.616737	0.795205	0.447242
9	speaker_24_center_83.mov	0.705923	0.658382	0.610645	0.697415	0.411988
10	speaker_27_center_83.mov	0.753417	0.708372	0.654608	0.816416	0.504743

[2023-12-16 19:13:30] Точность по отдельным персональным качествам личности человека …

	Openness	Conscientiousness	Extraversion	Agreeableness	Non-Neuroticism	Mean
Metrics
MAE	0.0673	0.0789	0.1325	0.102	0.1002	0.0962
Accuracy	0.9327	0.9211	0.8675	0.898	0.8998	0.9038

[2023-12-16 19:13:30] Средняя средних абсолютных ошибок: 0.0962, средняя точность: 0.9038 …

Лог файлы успешно сохранены …

— Время выполнения: 416.453 сек. —

[7]:

True

Для прогнозирования потребительских предпочтений в промышленных товарах необходимо знать коэффициенты корреляции, определяющие взаимосвязь между персональными качествами личности человека и предпочтениями в товарах или услугах.

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

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.

Пользователь может установить свои коэффициенты корреляции.

Прогнозирование потребительских предпочтений на характеристики атомобиля

[8]:

# Загрузка датафрейма с коэффициентами корреляции
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

[8]:

	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

[9]:

_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)

# 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

[9]:

	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	0.758	0.693	0.650	0.745	0.489	Safe and reliable	Practical and easy to use	Economical/low cost	Conscientiousness	Agreeableness	Openness
2	speaker_06_center_83.mov	0.682	0.654	0.607	0.731	0.418	Safe and reliable	Practical and easy to use	Economical/low cost	Conscientiousness	Agreeableness	Openness
3	speaker_07_center_83.mov	0.666	0.657	0.568	0.685	0.378	Safe and reliable	Practical and easy to use	Economical/low cost	Conscientiousness	Agreeableness	Openness
4	speaker_10_center_83.mov	0.694	0.596	0.571	0.662	0.349	Safe and reliable	Practical and easy to use	Economical/low cost	Conscientiousness	Agreeableness	Openness
5	speaker_11_center_83.mov	0.713	0.595	0.572	0.717	0.378	Safe and reliable	Practical and easy to use	Economical/low cost	Agreeableness	Conscientiousness	Openness
6	speaker_15_center_83.mov	0.664	0.670	0.604	0.696	0.400	Safe and reliable	Practical and easy to use	Economical/low cost	Conscientiousness	Agreeableness	Openness
7	speaker_19_center_83.mov	0.761	0.653	0.651	0.789	0.460	Safe and reliable	Practical and easy to use	Economical/low cost	Agreeableness	Conscientiousness	Openness
8	speaker_23_center_83.mov	0.693	0.683	0.617	0.795	0.447	Safe and reliable	Practical and easy to use	Economical/low cost	Agreeableness	Conscientiousness	Openness
9	speaker_24_center_83.mov	0.706	0.658	0.611	0.697	0.412	Safe and reliable	Practical and easy to use	Economical/low cost	Conscientiousness	Agreeableness	Openness
10	speaker_27_center_83.mov	0.753	0.708	0.655	0.816	0.505	Safe and reliable	Practical and easy to use	Economical/low cost	Agreeableness	Conscientiousness	Openness

Прогнозирование потребительских предпочтений на характеристики мобильного устройства

В качестве примера предлагается использование коэффициентов корреляции между персональными качествами человека и характеристиками мобильного устройства, представленными в статье:

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.

[10]:

# Загрузка датафрейма с коэффициентами корреляции
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

[10]:

	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

[11]:

_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)

# 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

[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	0.758	0.693	0.650	0.745	0.489	Game Casino	Communication	Game Board	Extraversion	Agreeableness	Conscientiousness
2	speaker_06_center_83.mov	0.682	0.654	0.607	0.731	0.418	Game Casino	Communication	Game Board	Agreeableness	Extraversion	Conscientiousness
3	speaker_07_center_83.mov	0.666	0.657	0.568	0.685	0.378	Game Casino	Communication	Game Board	Agreeableness	Conscientiousness	Extraversion
4	speaker_10_center_83.mov	0.694	0.596	0.571	0.662	0.349	Game Casino	Communication	Game Board	Agreeableness	Extraversion	Conscientiousness
5	speaker_11_center_83.mov	0.713	0.595	0.572	0.717	0.378	Game Casino	Communication	Game Board	Agreeableness	Extraversion	Conscientiousness
6	speaker_15_center_83.mov	0.664	0.670	0.604	0.696	0.400	Game Casino	Communication	Game Board	Extraversion	Agreeableness	Conscientiousness
7	speaker_19_center_83.mov	0.761	0.653	0.651	0.789	0.460	Game Casino	Communication	Game Board	Agreeableness	Extraversion	Conscientiousness
8	speaker_23_center_83.mov	0.693	0.683	0.617	0.795	0.447	Game Casino	Communication	Game Board	Agreeableness	Extraversion	Conscientiousness
9	speaker_24_center_83.mov	0.706	0.658	0.611	0.697	0.412	Game Casino	Communication	Game Board	Extraversion	Agreeableness	Conscientiousness
10	speaker_27_center_83.mov	0.753	0.708	0.655	0.816	0.505	Game Casino	Communication	Game Board	Agreeableness	Extraversion	Conscientiousness

`MuPTA` (en)

[12]:

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']['sberdisk']
res_load_model_weights_hc = _b5.load_audio_model_weights_hc(url = url)

url = _b5.weights_for_big5_['audio'][corpus]['nn']['sberdisk']
res_load_model_weights_nn = _b5.load_audio_model_weights_nn(url = url)

# 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']['sberdisk']
res_load_model_weights_hc = _b5.load_video_model_weights_hc(url = url)

url = _b5.weights_for_big5_['video'][corpus]['fe']['sberdisk']
res_load_model_weights_deep_fe = _b5.load_video_model_weights_deep_fe(url = url)

url = _b5.weights_for_big5_['video'][corpus]['nn']['sberdisk']
res_load_model_weights_nn = _b5.load_video_model_weights_nn(url = url)

# 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']['sberdisk']
res_load_text_model_weights_hc_fi = _b5.load_text_model_weights_hc(url = url)

url = _b5.weights_for_big5_['text'][corpus]['nn']['sberdisk']
res_load_text_model_weights_nn_fi = _b5.load_text_model_weights_nn(url = url)

# 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']['sberdisk']
res_load_avt_model_weights_b5 = _b5.load_avt_model_weights_b5(url = url)

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)

[2023-12-16 19:20:55] Извлечение признаков (экспертных и нейросетевых) из текста …

[2023-12-16 19:20:57] 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	0.564985	0.539052	0.440615	0.59251	0.488763
2	speaker_06_center_83.mov	0.650774	0.663849	0.607308	0.643847	0.620627
3	speaker_07_center_83.mov	0.435976	0.486683	0.313828	0.415446	0.396618
4	speaker_10_center_83.mov	0.498542	0.511243	0.412592	0.468947	0.44399
5	speaker_11_center_83.mov	0.394776	0.341608	0.327082	0.427304	0.354936
6	speaker_15_center_83.mov	0.566107	0.543811	0.492766	0.587411	0.499433
7	speaker_19_center_83.mov	0.506271	0.438215	0.430894	0.456177	0.44075
8	speaker_23_center_83.mov	0.486463	0.521755	0.309894	0.432291	0.433601
9	speaker_24_center_83.mov	0.417404	0.473339	0.320714	0.445086	0.414649
10	speaker_27_center_83.mov	0.526112	0.661107	0.443167	0.558965	0.554224

[2023-12-16 19:20:57] Точность по отдельным персональным качествам личности человека …

	Openness	Conscientiousness	Extraversion	Agreeableness	Non-Neuroticism	Mean
Metrics
MAE	0.1727	0.1672	0.1661	0.2579	0.107	0.1742
Accuracy	0.8273	0.8328	0.8339	0.7421	0.893	0.8258

[2023-12-16 19:20:57] Средняя средних абсолютных ошибок: 0.1742, средняя точность: 0.8258 …

Лог файлы успешно сохранены …

— Время выполнения: 379.936 сек. —

[12]:

True

Для прогнозирования потребительских предпочтений в промышленных товарах необходимо знать коэффициенты корреляции, определяющие взаимосвязь между персональными качествами личности человека и предпочтениями в товарах или услугах.

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

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.

Пользователь может установить свои коэффициенты корреляции.

Прогнозирование потребительских предпочтений на характеристики атомобиля

[13]:

# Загрузка датафрейма с коэффициентами корреляции
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

[13]:

	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

[14]:

_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)

# 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

[14]:

	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	0.565	0.539	0.441	0.593	0.489	Practical and easy to use	Economical/low cost	Family friendly	Agreeableness	Openness	Non-Neuroticism
2	speaker_06_center_83.mov	0.651	0.664	0.607	0.644	0.621	Safe and reliable	Practical and easy to use	Economical/low cost	Conscientiousness	Agreeableness	Openness
3	speaker_07_center_83.mov	0.436	0.487	0.314	0.415	0.397	Classic car features	Fashion and attention	Luxury additions	Agreeableness	Conscientiousness	Openness
4	speaker_10_center_83.mov	0.499	0.511	0.413	0.469	0.444	Classic car features	Fashion and attention	Luxury additions	Agreeableness	Conscientiousness	Openness
5	speaker_11_center_83.mov	0.395	0.342	0.327	0.427	0.355	Classic car features	Fashion and attention	Luxury additions	Agreeableness	Conscientiousness	Openness
6	speaker_15_center_83.mov	0.566	0.544	0.493	0.587	0.499	Practical and easy to use	Economical/low cost	Family friendly	Agreeableness	Openness	Non-Neuroticism
7	speaker_19_center_83.mov	0.506	0.438	0.431	0.456	0.441	Classic car features	Fashion and attention	Luxury additions	Agreeableness	Conscientiousness	Openness
8	speaker_23_center_83.mov	0.486	0.522	0.310	0.432	0.434	Classic car features	Fashion and attention	Luxury additions	Agreeableness	Conscientiousness	Openness
9	speaker_24_center_83.mov	0.417	0.473	0.321	0.445	0.415	Classic car features	Fashion and attention	Luxury additions	Agreeableness	Conscientiousness	Openness
10	speaker_27_center_83.mov	0.526	0.661	0.443	0.559	0.554	Safe and reliable	Practical and easy to use	Economical/low cost	Conscientiousness	Agreeableness	Non-Neuroticism

Прогнозирование потребительских предпочтений на характеристики мобильного устройства

В качестве примера предлагается использование коэффициентов корреляции между персональными качествами человека и характеристиками мобильного устройства, представленными в статье:

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.

[15]:

# Загрузка датафрейма с коэффициентами корреляции
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

[15]:

	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

[16]:

_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)

# 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

[16]:

	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	0.565	0.539	0.441	0.593	0.489	Communication	Health and Fitness	Media and Video	Agreeableness	Openness	Non-Neuroticism
2	speaker_06_center_83.mov	0.651	0.664	0.607	0.644	0.621	Game Casino	Communication	Game Trivia	Non-Neuroticism	Extraversion	Conscientiousness
3	speaker_07_center_83.mov	0.436	0.487	0.314	0.415	0.397	Media and Video	Entertainment	Health and Fitness	Agreeableness	Conscientiousness	Extraversion
4	speaker_10_center_83.mov	0.499	0.511	0.413	0.469	0.444	Media and Video	Entertainment	Health and Fitness	Agreeableness	Conscientiousness	Extraversion
5	speaker_11_center_83.mov	0.395	0.342	0.327	0.427	0.355	Media and Video	Entertainment	Health and Fitness	Conscientiousness	Agreeableness	Extraversion
6	speaker_15_center_83.mov	0.566	0.544	0.493	0.587	0.499	Health and Fitness	Media and Video	News and Magazines	Agreeableness	Openness	Extraversion
7	speaker_19_center_83.mov	0.506	0.438	0.431	0.456	0.441	Media and Video	Entertainment	Health and Fitness	Conscientiousness	Agreeableness	Extraversion
8	speaker_23_center_83.mov	0.486	0.522	0.310	0.432	0.434	Media and Video	Entertainment	Health and Fitness	Agreeableness	Conscientiousness	Extraversion
9	speaker_24_center_83.mov	0.417	0.473	0.321	0.445	0.415	Media and Video	Entertainment	Health and Fitness	Agreeableness	Conscientiousness	Extraversion
10	speaker_27_center_83.mov	0.526	0.661	0.443	0.559	0.554	Game Casino	Game Educational	Game Trivia	Non-Neuroticism	Conscientiousness	Agreeableness

Решение практической задачи 2

Задача: прогнозирование потребительских предпочтений на промышленные товары

FI V2

Прогнозирование потребительских предпочтений на характеристики атомобиля

Прогнозирование потребительских предпочтений на характеристики мобильного устройства

MuPTA (ru)

Прогнозирование потребительских предпочтений на характеристики атомобиля

Прогнозирование потребительских предпочтений на характеристики мобильного устройства

MuPTA (en)

Прогнозирование потребительских предпочтений на характеристики атомобиля

Прогнозирование потребительских предпочтений на характеристики мобильного устройства

`FI V2`

`MuPTA` (ru)

`MuPTA` (en)