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.
FI V2
[2]:
# Import required tools
import os
import pandas as pd
# Module import
from oceanai.modules.lab.build import Run
# Creating an instance of a class
_b5 = Run(lang = 'en')
# Core setup
_b5.path_to_save_ = './models' # Directory to save the models
_b5.chunk_size_ = 2000000 # File download size from network in one step
corpus = 'fi'
# Building audio models
res_load_model_hc = _b5.load_audio_model_hc()
res_load_model_nn = _b5.load_audio_model_nn()
# Loading audio model weights
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)
# Loading audio model weights
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()
# Loading video model weights
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)
# Loading a dictionary with hand-crafted features (text modality)
res_load_text_features = _b5.load_text_features()
# Building text models
res_setup_translation_model = _b5.setup_translation_model()
res_setup_translation_model = _b5.setup_bert_encoder()
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)
# Loading text model weights
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)
# Building model for multimodal information fusion
res_load_avt_model_b5 = _b5.load_avt_model_b5()
# Loading model weights for multimodal information fusion
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
# Loading 10 test files from the First Impressions V2 corpus
# 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)
# Getting scores
_b5.path_to_dataset_ = PATH_TO_DIR # Dataset directory
_b5.ext_ = ['.mp4'] # Search file extensions
# Full path to the file with ground truth scores for accuracy calculation
url_accuracy = _b5.true_traits_[corpus]['sberdisk']
_b5.get_avt_predictions(url_accuracy = url_accuracy, lang = 'en')
[2023-12-16 19:05:15] Feature extraction (hand-crafted and deep) from text …
[2023-12-16 19:05:17] Getting scores and accuracy calculation (multimodal fusion) …
10 from 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] Trait-wise accuracy …
| 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] Mean absolute errors: 0.0669, average accuracy: 0.9331 …
Log files saved successfully …
— Runtime: 64.147 sec. —
[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:
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 | 0.581 | 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 |
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:
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 | 0.581 | 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
# Module import
from oceanai.modules.lab.build import Run
# Creating an instance of a class
_b5 = Run(lang = 'en')
corpus = 'mupta'
lang = 'ru'
# Core setup
_b5.path_to_save_ = './models' # Directory to save the models
_b5.chunk_size_ = 2000000 # File download size from network in one step
# Building audio models
res_load_model_hc = _b5.load_audio_model_hc()
res_load_model_nn = _b5.load_audio_model_nn()
# Loading audio model weights
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)
# Building video models
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()
# Loading video model weights
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)
# Loading a dictionary with hand-crafted features (text modality)
res_load_text_features = _b5.load_text_features()
# Building text models
res_setup_translation_model = _b5.setup_translation_model()
res_setup_translation_model = _b5.setup_bert_encoder()
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)
# Loading text model weights
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)
# Building model for multimodal information fusion
res_load_avt_model_b5 = _b5.load_avt_model_b5()
# Loading model weights for multimodal information fusion
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
# Loading 10 test files from the MuPTA corpus
# 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)
# Getting scores
_b5.path_to_dataset_ = PATH_TO_DIR # Dataset directory
_b5.ext_ = ['.mov'] # Search file extensions
# Full path to the file with ground truth scores for accuracy calculation
url_accuracy = _b5.true_traits_['mupta']['sberdisk']
_b5.get_avt_predictions(url_accuracy = url_accuracy, lang = lang)
[2023-12-16 19:13:25] Feature extraction (hand-crafted and deep) from text …
[2023-12-16 19:13:30] Getting scores and accuracy calculation (multimodal fusion) …
10 from 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] Trait-wise accuracy …
| 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] Mean absolute errors: 0.0962, average accuracy: 0.9038 …
Log files saved successfully …
— Runtime: 416.453 sec. —
[7]:
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:
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
[8]:
# 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
[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)
# 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
[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 |
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:
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]:
# 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
[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)
# 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 | 0.758 | 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
# Module import
from oceanai.modules.lab.build import Run
# Creating an instance of a class
_b5 = Run(lang = 'en')
corpus = 'fi'
lang = 'en'
# Core setup
_b5.path_to_save_ = './models' # Directory to save the models
_b5.chunk_size_ = 2000000 # File download size from network in one step
# Building audio models
res_load_model_hc = _b5.load_audio_model_hc()
res_load_model_nn = _b5.load_audio_model_nn()
# Loading audio model weights
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)
# Building video models
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()
# Loading video model weights
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)
# Loading a dictionary with hand-crafted features (text modality)
res_load_text_features = _b5.load_text_features()
# Building text models
res_setup_translation_model = _b5.setup_translation_model()
res_setup_translation_model = _b5.setup_bert_encoder()
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)
# Loading text model weights
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)
# Building model for multimodal information fusion
res_load_avt_model_b5 = _b5.load_avt_model_b5()
# Building model for multimodal information fusion
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
# Loading 10 test files from the MuPTA corpus
# 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)
# Getting scores
_b5.path_to_dataset_ = PATH_TO_DIR # Dataset directory
_b5.ext_ = ['.mov'] # Search file extensions
# Full path to the file with ground truth scores for accuracy calculation
url_accuracy = _b5.true_traits_['mupta']['sberdisk']
_b5.get_avt_predictions(url_accuracy = url_accuracy, lang = lang)
[2023-12-16 19:20:55] Feature extraction (hand-crafted and deep) from text …
[2023-12-16 19:20:57] Getting scores and accuracy calculation (multimodal fusion) …
10 from 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] Trait-wise accuracy …
| 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] Mean absolute errors: 0.1742, average accuracy: 0.8258 …
Log files saved successfully …
— Runtime: 379.936 sec. —
[12]:
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:
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
[13]:
# 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
[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)
# 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
[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 |
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:
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]:
# 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
[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)
# 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
[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 |