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))