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

# # Installation

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# run this cell to install pycaret in Google Colab
# !pip install pycaret


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# If you are using Jupyter notebook, you can pip install pycaret using jupyter notebook or command line
# pip install pycaret


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from pycaret.utils import version
version()


# # 1. Importing Dataset

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


# # 2. Iteration 1: (without preprocessing)

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from pycaret.regression import *
reg1 = setup(data, target = 'Price', session_id=786)


# ## 2.1. Compare Models

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compare_models(blacklist = ['tr', 'catboost'])


# ## 2.2 Create Model

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lr = create_model('lr')


# # 2.3 Plot Model

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plot_model(lr)


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plot_model(lr, plot = 'error')


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plot_model(lr, plot = 'feature')


# # 3. Iteration 2: (with preprocessing)

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# profile = True
data = get_data('diamond', profile = True)


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reg2 = setup(data, target = 'Price', session_id=786,
             transform_target = True,
             bin_numeric_features=['Carat Weight'],
             remove_multicollinearity=True,
             feature_interaction=True)


# ## 3.1 Create Model

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lr2 = create_model('lr')


# ## 3.2. Plot Model

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plot_model(lr2)


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plot_model(lr2, plot = 'error')


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plot_model(lr2, plot = 'feature')


# # 4. Finalize Model

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holdout_pred = predict_model(lr2)


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final_lr = finalize_model(lr2)


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print(final_lr)


# # 5. Save Model

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save_model(final_lr, 'lr_smith_demo')


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lr_loaded = load_model('lr_smith_demo')


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print(lr_loaded)


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predictions = predict_model(lr_loaded, data=data)
predictions.head()


# # 6. Deploy Model

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


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predictions2 = predict_model('lr_smith_demo', data=data)
predictions2.head()


# # Learning Resources:
# 
# - PyCaret Regression Module : https://www.pycaret.org/regression
# - Regression Tutorial (Level Beginner) : https://pycaret.org/reg101/
# - Regression Tutorial (Level Intermediate) : https://pycaret.org/reg102/