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

# # PyCaret 2 Clustering Example
# This notebook is created using PyCaret 2.0. Last updated : 28-07-2020

# In[ ]:


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


# # 1. Loading Dataset

# In[ ]:


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


# # 2. Initialize Setup

# In[ ]:


from pycaret.clustering import *
clu1 = setup(data, ignore_features = ['Country Name'], session_id=123, log_experiment=True, log_plots = True, experiment_name='health1')


# # 3. Create Model

# In[ ]:


models()


# In[ ]:


kmeans = create_model('kmeans', num_clusters = 4)


# In[ ]:


kmodes = create_model('kmodes', num_clusters = 4)


# # 4. Assign Labels

# In[ ]:


kmeans_results = assign_model(kmeans)
kmeans_results.head()


# # 5. Analyze Model

# In[ ]:


plot_model(kmeans)


# In[ ]:


plot_model(kmeans, feature = 'Country Name', label=True)


# In[ ]:


plot_model(kmeans, plot = 'tsne')


# In[ ]:


plot_model(kmeans, plot = 'elbow')


# In[ ]:


plot_model(kmeans, plot = 'silhouette')


# In[ ]:


plot_model(kmeans, plot = 'distance')


# In[ ]:


plot_model(kmeans, plot = 'distribution')


# # 6. Predict Model

# In[ ]:


pred_new = predict_model(kmeans, data=data)
pred_new.head()


# # 7. Save / Load Model

# In[ ]:


save_model(kmeans, model_name='kmeans')


# In[ ]:


loaded_kmeans = load_model('kmeans')
print(loaded_kmeans)


# In[ ]:


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


# In[ ]:


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


# # 8. Deploy Model

# In[ ]:


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


# # 9. Get Config / Set Config

# In[ ]:


X = get_config('X')
X.head()


# In[ ]:


get_config('seed')


# In[ ]:


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


# In[ ]:


get_config('seed')


# # 10. Get System Logs

# In[ ]:


get_system_logs()


# # 11. MLFlow UI

# In[ ]:


get_ipython().system('mlflow ui')


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