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

# # Getting Started

# This getting started notebook is an overview of the functionality in this repository.  Note that this notebook is not runnable, it has a high-level overview of the APIs available and contains links to other notebooks in the repository.

# ## Installation

# Use the following `pip` commands to install the Responsible AI Toolbox.
# 
# If running in jupyter, please make sure to restart the jupyter kernel after installing.

# !pip install raiwidgets

# ## Dependencies

# Please make sure to have the latest version of pandas installed if you are planning to use the error analysis component.

# !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, the RAIInsights and ResponsibleAIDashboard must be imported.

# ```Python
# from raiwidgets import ResponsibleAIDashboard
# from responsibleai import RAIInsights
# ```

# Users will need to load a dataset, spit it into train and test datasets, and train a model on the training dataset.

# 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.
# 
# Users can also specify categorical features via the `categorical_features` parameter.
# 
# Using the `FeatureMetadata` container, you can declare an `identity_feature`, and specify features to withhold from the model via the `dropped_features` parameter. The `FeatureMetadata` serves as an input argument for `RAIInsights`.

# ```Python
# from responsibleai.feature_metadata import FeatureMetadata
# # Add 's1' as an identity feature, set 'age' as a dropped feature
# feature_metadata = FeatureMetadata(identity_feature_name='s1', dropped_features=['age'])
# 
# task_type = 'regression'
# 
# rai_insights = RAIInsights(model, train_data, test_data, target_feature, task_type, categorical_features=[], feature_metadata=feature_metadata)
# ```

# The Interpretability and Error Analysis components can be added to the dashboard without any additional arguments.
# 
# For an example, please see the [census classification model debugging notebook](https://github.com/microsoft/responsible-ai-toolbox/blob/main/notebooks/responsibleaidashboard/responsibleaidashboard-census-classification-model-debugging.ipynb).

# ```Python
# 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.
# 
# For an example, please see the [diabetes decision making notebook](https://github.com/microsoft/responsible-ai-toolbox/blob/main/notebooks/responsibleaidashboard/responsibleaidashboard-diabetes-decision-making.ipynb).

# ```Python
# rai_insights.causal.add(treatment_features=['bmi', 'bp', 's2'])
# ```

# 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.
# 
# For an example, please see the [housing classification model debugging notebook](https://github.com/microsoft/responsible-ai-toolbox/blob/main/notebooks/responsibleaidashboard/responsibleaidashboard-housing-classification-model-debugging.ipynb).

# ```Python
# rai_insights.counterfactual.add(total_CFs=20, desired_class='opposite')
# ```

# In a regression situation, `desired_range` must specify the minimum and maximum label that the generated counterfactuals can have.
# For an example, please see the [diabetes regression model debugging notebook](https://github.com/microsoft/responsible-ai-toolbox/blob/main/notebooks/responsibleaidashboard/responsibleaidashboard-diabetes-regression-model-debugging.ipynb).

# ```Python
# rai_insights.counterfactual.add(total_CFs=20, desired_range=[50, 120])
# ```

# ## 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.

# ```Python
# rai_insights.compute()
# ```

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

# ```Python
# 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.