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

# # PiML Toolbox: Uploading Custom Data in Two Ways
# 
# Other than the built-in datasets (e.g. BikeSharing, CaliforniaHousing, TaiwanCredit) for demo purpose, PiML supports custom data uploading for model development and validation. 
# 
# This example notebook demonstrates how to upload/read custom data in two ways. 
# 
# 1. **Upload new data** by the `piml.Experiment.data_loader()` widget (with file size limit 10MB)
# 
# 2. **Manually read data** by `panda.read_csv()`, then register to `piml.Experiment`. 
# 
# For simplicity, we employ the CASP dataset you may download from `https://archive.ics.uci.edu/ml/machine-learning-databases/00265/CASP.csv` (~3.4MB, with details [here](https://archive.ics.uci.edu/ml/datasets/Physicochemical+Properties+of+Protein+Tertiary+Structure) ) and save it to your local drive. 
#  

# # Stage 0: Install PiML package on Google Colab
# 
# 1. Run `!pip install piml` to install the latest version of PiML
# 2. In Colab, you'll need restart the runtime in order to use newly installed PiML version.

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get_ipython().system('pip install piml')


# # Stage 1: Initialize an experiment, Load and Prepare data <a name="expdata"></a>

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from piml import Experiment
exp = Experiment()


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# The first way: upload new data from your local drive by the widget (with file size limit 10MB)
# Choose "Upload new data"
exp.data_loader() 


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# The second way: manually read data from your local drive or URL, then register to piml.Experiment
import pandas as pd
data = pd.read_csv("https://archive.ics.uci.edu/ml/machine-learning-databases/00265/CASP.csv") 
exp.data_loader(data=data)


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exp.data_summary()


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# Select RMSD as the target variable and click "UPDATE"; this is a regression task
exp.data_prepare()


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exp.feature_select()


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exp.eda()


# # Stage 2. Train intepretable models <a name="modeltrain"></a>
# 
# 

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# Choose GLM, GAM, Tree models with default settings, click "RUN" to train; 
# After training is finished, register the three trained models one by one.
exp.model_train()


# # Stage 3. Explain and Interpret <a name="modelinterpret"></a>

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# Model-agnostic post-hoc explanation by Permutation Feature Importance, PDP (1D and 2D) vs. ALE (1D and 2D), LIME vs. SHAP
exp.model_explain()


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# Model-specific inherent interpretation including feature importance, main effects and pairwise interactions.
exp.model_interpret()


# # Stage 4. Diagnose and Compare

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exp.model_diagnose()


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exp.model_compare()


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