# check version
from pycaret.utils import version
version()

from pycaret.datasets import get_data
data = get_data('insurance')

from pycaret.regression import *
reg1 = setup(data, target = 'charges', session_id=123, log_experiment=True, experiment_name='insurance1')

best_model = compare_models(fold=5)

lightgbm = create_model('lightgbm')

lgbm= []

import numpy as np
for i in np.arange(0.1,1,0.1):
    lgbm.append(create_model('lightgbm', learning_rate=i, verbose=False))

print(len(lgbm))

from interpret.glassbox import ExplainableBoostingRegressor
ebm = ExplainableBoostingRegressor()
ebm = create_model(ebm, fold = 3)

from interpret import show
ebm_global = ebm.explain_global()
show(ebm_global)

tuned_lightgbm = tune_model(lightgbm, n_iter=50, optimize = 'MAE')

tuned_lightgbm

dt = create_model('dt')

bagged_dt = ensemble_model(dt, n_estimators=50)

boosted_dt = ensemble_model(dt, method = 'Boosting')

blender = blend_models()

stacker = stack_models(estimator_list = compare_models(n_select=5, fold = 5, whitelist = models(type='ensemble').index.tolist()))

plot_model(dt)

plot_model(dt, plot = 'error')

plot_model(dt, plot = 'feature')

evaluate_model(dt)

interpret_model(lightgbm)

interpret_model(lightgbm, plot = 'correlation')

interpret_model(lightgbm, plot = 'reason', observation = 12)

best = automl(optimize = 'MAE')
best

pred_holdouts = predict_model(lightgbm)
pred_holdouts.head()

new_data = data.copy()
new_data.drop(['charges'], axis=1, inplace=True)
predict_new = predict_model(best, data=new_data)
predict_new.head()

save_model(best, model_name='best-model')

loaded_bestmodel = load_model('best-model')
print(loaded_bestmodel)

from sklearn import set_config
set_config(display='diagram')
loaded_bestmodel[0]

from sklearn import set_config
set_config(display='text')

deploy_model(best, model_name = 'best-aws', authentication = {'bucket' : 'pycaret-test'})

X_train = get_config('X_train')
X_train.head()

get_config('seed')

from pycaret.regression import set_config
set_config('seed', 999)

get_config('seed')

get_system_logs()

get_logs()

!mlflow ui