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XAI - An industry-ready machine learning library that ensures explainable AI by design

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# XAI - An eXplainability toolbox for machine learning

XAI is a Machine Learning library that is designed with AI explainability in its core. XAI contains various tools that enable for analysis and evaluation of data and models. The XAI library is maintained by [The Institute for Ethical AI & ML](http://ethical.institute/), and it was developed based on the [8 principles for Responsible Machine Learning](http://ethical.institute/principles.html).

You can find the documentation at [https://ethicalml.github.io/xai/index.html](https://ethicalml.github.io/xai/index.html). You can also check out our [talk at Tensorflow London](https://www.youtube.com/watch?v=GZpfBhQJ0H4) where the idea was first conceived - the talk also contains an insight on the definitions and principles in this library.

# 0.0.4 - ALPHA Version

This library is currently in early stage developments and hence it will be quite unstable due to the fast updates. It is important to bare this in mind if using it in production.

## What do we mean by eXplainable AI?

We see the challenge of explainability as more than just an algorithmic challenge, which requires a combination of data science best practices with domain-specific knowledge. The XAI library is designed to empower machine learning engineers and relevant domain experts to analyse the end-to-end solution and identify discrepancies that may result in sub-optimal performance relative to the objectives required. More broadly, the XAI library is designed using the 3-steps of explainable machine learning, which involve 1) data analysis, 2) model evaluation, and 3) production monitoring.

We provide a visual overview of these three steps mentioned above in this diagram:

<img width="100%" src="images/bias.png">

# XAI Quickstart

## Installation

The XAI package is on PyPI. To install you can run:

```
pip install xai
```

Alternatively you can install from source by cloning the repo and running:

```
python setup.py install
```

## Usage

You can find example usage in the examples folder.

### 1) Data Analysis

With XAI you can identify imbalances in the data. For this, we will load the census dataset from the XAI library.

``` python
import xai.data
df = xai.data.load_census()
df.head()
```
<img width="100%" src="images/readme-1.png">

#### View class imbalances for protected columns
``` python
protected_cols = ["gender", "ethnicity", "age"]
ims = xai.show_imbalances(df, protected_cols)
```
<img width="100%" src="images/readme-2.png">

#### View imbalance of one column
``` python
im = xai.show_imbalance(df, "gender")
```
<img width="100%" src="images/readme-3.png">

#### View imbalance of one column intersected with another
``` python
im = xai.show_imbalance(df, "gender", cross=["loan"])
```
<img width="100%" src="images/readme-4.png">

#### Balance the class using upsampling and/or downsampling
``` python
bal_df = xai.balance(df, "gender", cross=["loan"], upsample=1.0)
```
<img width="100%" src="images/readme-5.png">

#### Create a balanced test-train split (should be done pre-balancing)
``` python
# Balanced train-test split with minimum 300 examples of
# the cross of the target y and the column gender
x_train, y_train, x_test, y_test = xai.balanced_train_test_split(
x, y, cross=["gender"],
categorical_cols=categorical_cols, min_per_class=300)

# Visualise the imbalances of gender and the target
df_test = x_test.copy()
df_test["loan"] = y_test
_= xai.show_imbalance(df_test, "gender", cross=["loan"], categorical_cols=categorical_cols)
```
<img width="100%" src="images/readme-16.png">

### 2) Model Evaluation

We are able to also analyse the interaction between inference results and input features. For this, we will train a single layer deep learning model.

```
model = build_model(proc_df.drop("loan", axis=1))

model.fit(f_in(x_train), y_train, epochs=50, batch_size=512)

probabilities = model.predict(f_in(x_test))
predictions = list((probabilities >= 0.5).astype(int).T[0])
```
<img width="100%" src="images/readme-15.png">

#### Visualise permutation feature importance
``` python
def get_avg(x, y):
return model.evaluate(f_in(x), y, verbose=0)[1]

imp = xai.feature_importance(x_test, y_test, get_avg)

imp.head()
```
<img width="100%" src="images/readme-6.png">

#### Identify metric imbalances against all test data
``` python
_= xai.metrics_imbalance(
x_test,
y_test,
probabilities)
```
<img width="100%" src="images/readme-7.png">

#### Identify metric imbalances grouped by protected columns
``` python
_= xai.metrics_imbalances(
x_test,
y_test,
probabilities,
columns=protected,
categorical_cols=categorical_cols)
```
<img width="100%" src="images/readme-8.png">

#### Visualise the ROC curve against all test data
``` python
_= xai.roc_imbalance(
x_test,
y_test,
probabilities)
```
<img width="100%" src="images/readme-9.png">

#### Visualise the ROC curves grouped by protected columns
``` python
_= xai.roc_imbalances(
x_test,
y_test,
probabilities,
columns=protected,
categorical_cols=categorical_cols)
```
<img width="100%" src="images/readme-10.png">

#### Visualise the precision-recall curve by protected columns
``` python
_= xai.pr_imbalances(
x_test,
y_test,
probabilities,
columns=protected,
categorical_cols=categorical_cols)
```
<img width="100%" src="images/readme-11.png">

#### Visualise accuracy grouped by probability buckets
``` python
d = xai.smile_imbalance(
y_test,
probabilities)
```
<img width="100%" src="images/readme-12.png">

#### Visualise statistical metrics grouped by probability buckets
``` python
d = xai.smile_imbalance(
y_test,
probabilities,
display_breakdown=True)
```
<img width="100%" src="images/readme-13.png">

#### Visualise benefits of adding manual review on probability thresholds
``` python
d = xai.smile_imbalance(
y_test,
probabilities,
bins=9,
threshold=0.75,
manual_review=0.375,
display_breakdown=False)
```
<img width="100%" src="images/readme-14.png">

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