import pandas as pd
from pycaret.classification import *
data = pd.read_csv('data/data_all.csv')
print(data)
m_setup = setup(data=data, target='label', normalize=True, #bin_numeric_features=['illum'],
feature_interaction=False,
feature_ratio=False,
trigonometry_features=True,
use_gpu=True)
m_setup
best_model = compare_models(n_select=6)
# tuned_top = [tune_model(x) for x in best_model]
bagged_top = [ensemble_model(x) for x in tuned_top]
blender = blend_models(estimator_list=best_model)
best = automl(optimize = 'F1')
m = create_model('rf')
m_tune = tune_model(m, optimize='Kappa')
# plot_model(tuned_dt, plot='auc')
evaluate_model(m)
plot_model(tuned_dt, plot='feature')