import time
from data.load_dataframe import load_profiles
from utils.models_utils import lr, knr
start_time = time.time()
profiles = load_profiles(income=True)
"""
Age (Regression)
"""
# print(profiles.corrwith(profiles['age']))
current_guess = ['age']
all_features = [
'high_income', 'middle_income', 'low_income',
'has_high_academic_degree', 'has_graduated', 'is_studying',
'drinks_code', 'drugs_code', 'smokes_code',
'has_kids', 'has_no_kids', 'wants_kids', 'doesnt_want_kids',
]
lr(profiles, all_features, current_guess)
# knr(profiles, all_features, current_guess, 41, plot_best_k=1)
print('')
print("%s seconds" % (time.time() - start_time))
import time
from data.load_dataframe import load_profiles
from utils.models_utils import svc, knc, nbc
start_time = time.time()
profiles = load_profiles(income=False)
"""
Sex Code (Classification)
"""
# print(profiles.corrwith(profiles['sex_code']))
current_guess = ['sex_code']
all_features = [
'height',
'has_fit_body_type', 'has_chubby_body_type', 'has_thin_body_type', 'has_average_body_type',
'eats_anything', 'eats_vegetarian', 'eats_vegan',
'stem_career', 'health_career', 'education_career',
]
mclasses=['0 (male)', '1 (female)']
svc(profiles, all_features, current_guess, vector_kernel='linear', vector_c=4, vector_gamma=8, show_report=True, show_matrix=True, matrix_classes=mclasses)
# knc(profiles, all_features, current_guess, 28, show_report=True, plot_best_k=1)
# nbc(profiles, all_features, current_guess)
print('')
print("%s seconds" % (time.time() - start_time))