#!/usr/bin/env python
# coding: utf-8

# # PyCaret 2 Regression Example
# This notebook is created using PyCaret 2.0. Last updated : 31-07-2020

# In[1]:


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


# # 1. Loading Dataset

# In[2]:


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


# # 2. Initialize Setup

# In[3]:


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


# # 3. Compare Baseline

# In[4]:


best_model = compare_models(fold=5)


# # 4. Create Model

# In[5]:


lightgbm = create_model('lightgbm')


# In[6]:


import numpy as np
lgbms = [create_model('lightgbm', learning_rate=i) for i in np.arange(0.1,1,0.1)]


# In[8]:


print(len(lgbms))


# # 5. Tune Hyperparameters

# In[9]:


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


# In[10]:


tuned_lightgbm


# # 6. Ensemble Model

# In[11]:


dt = create_model('dt')


# In[12]:


bagged_dt = ensemble_model(dt, n_estimators=50)


# In[13]:


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


# # 7. Blend Models

# In[14]:


blender = blend_models()


# # 8. Stack Models

# In[15]:


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


# # 9. Analyze Model

# In[16]:


plot_model(dt)


# In[17]:


plot_model(dt, plot = 'error')


# In[18]:


plot_model(dt, plot = 'feature')


# In[19]:


evaluate_model(dt)


# # 10. Interpret Model

# In[20]:


interpret_model(lightgbm)


# In[21]:


interpret_model(lightgbm, plot = 'correlation')


# In[22]:


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


# # 11. AutoML()

# In[23]:


best = automl(optimize = 'MAE')
best


# # 12. Predict Model

# In[24]:


pred_holdouts = predict_model(lightgbm)
pred_holdouts.head()


# In[25]:


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


# # 13. Save / Load Model

# In[26]:


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


# In[27]:


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


# In[28]:


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


# In[29]:


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


# # 14. Deploy Model

# In[30]:


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


# # 15. Get Config / Set Config

# In[31]:


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


# In[32]:


get_config('seed')


# In[33]:


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


# In[34]:


get_config('seed')


# # 16. MLFlow UI

# In[ ]:


get_ipython().system('mlflow ui')


# # End
# Thank you. For more information / tutorials on PyCaret, please visit https://www.pycaret.org