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

# # Getting Started

# ## Installation

# Use the following `pip` commands to install the Responsible AI Toolbox.

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


# ## Dependencies

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get_ipython().system('pip install --upgrade pandas')


# ## Overview & Setup

# Responsible AI Toolbox is an interoperable, customizable tool that empowers machine learning practitioners to evaluate their models and data based on their place in the model lifecycle.
# 
# Users may select components whose functionality supports their current objectives. First, import the relevant objects.

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from raiwidgets import ResponsibleAIDashboard
from responsibleai import RAIInsights


# It is necessary to initialize a RAIInsights object upon which the different components can be loaded. `task_type` holds the string `'regression'` or `'classification'` depending on the developer's purpose.

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rai_insights = RAIInsights(model, train_data, test_data, target_feature, task_type, 
                               categorical_features=['f1', 'f2', 'f3'])


# The Interpretability and Error Analysis components can be added to the dashboard without any additional arguments:

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rai_insights.explainer.add()
rai_insights.error_analysis.add()


# The Causal Inferencing component must be added with a specification of the feature that would be changed as a treatment.

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rai_insights.causal.add(treatment_features=['f1', 'f2', 'f3'])


# The Counterfactuals component takes arguments specifying the number of counterfactuals to generate, the list of columns containing continuous values, and the desired label of the counterfactuals.

# In a classification situation, `desired_class` must specify the classification that the generated counterfactuals would fall into.

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rai_insights.counterfactual.add(total_CFs=20, desired_class='opposite', continuous_features=['f1', 'f2', 'f3'])


# In a regression situation, `desired_range` must specify the minimum and maximum label that the generated counterfactuals can have.

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rai_insights.counterfactual.add(total_CFs=20, desired_range=[10, 20], continuous_features=['f1', 'f2', 'f3'])


# ## Computing and Visualizing Insights

# After loading the components into the RAIInsights object, it is necessary to calculate values relevant to them, such as model metrics and counterfactuals.

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rai_insights.compute()


# Once the values for each component have been computed, they can be displayed by loading the RAIInsights object into a ResponsibleAIDashboard.

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ResponsibleAIDashboard(rai_insights)


# ## Learn More

# Visit the [GitHub](https://github.com/microsoft/responsible-ai-widgets) of Responsible AI Toolbox for more details, and take this [dashboard tour](./tour.ipynb) for an explanation of the different parts of each component.