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<h1 align="center">
<img width="700" src="main_page_banner.png" alt="explainX.ai">
<br>
</h1>
# explainX: Explain & Debug Any Machine Learning Model With A Single Line of Code
<img src="explainx_logo.png" align="right" width="150"/>

<p align="center">
<a href="https://www.python.org/downloads/"><img src="https://img.shields.io/badge/python-3.6%20|%203.7|%203.8-brightgreen.svg" alt="Python supported"></a>
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<a href="https://www.explainx.ai/"> <img src="https://img.shields.io/website?url=https%3A%2F%2Fwww.explainx.ai%2F" alt="explainx.ai website"></a>
</p>
#### ExplainX.ai is a model explainability/interpretability framework for data scientists and business users.

ExplainX.ai is a fast, scalable and end-to-end Explainable AI framework for data scientists & machine learning engineers.
[![Supported Python versions](https://img.shields.io/badge/python-3.6%20|%203.7|%203.8-brightgreen.svg)](https://pypi.org/project/explainx/)
[![Downloads](https://pepy.tech/badge/dalex)](https://pepy.tech/project/dalex)
![Maintenance](https://img.shields.io/maintenance/yes/2020?style=flat-square)
[![Website](https://img.shields.io/website?url=https%3A%2F%2Fwww.explainx.ai%2F)](https://www.explainx.ai/)

Understand overall model behavior, get the reasoning behind model predictions, remove biases and create convincing explanations for your business stakeholders with explainX. [![Tweet](https://img.shields.io/twitter/url/http/shields.io.svg?style=social)](https://twitter.com/intent/tweet?text=Explain%20any%20black-box%20Machine%20Learning%20model%20in%20just%20one%20line%20of%20code%21&url=https://www.explainx.ai&hashtags=xai,explainable_ai,explainable_machine_learning,trust_in_ai,transparent_ai)

Use explainX to understand overall model behavior, explain the "why" behind model predictions, remove biases and create convincing explanations for your business stakeholders. [![Tweet](https://img.shields.io/twitter/url/http/shields.io.svg?style=social)](https://twitter.com/intent/tweet?text=Explain%20any%20black-box%20Machine%20Learning%20model%20in%20just%20one%20line%20of%20code%21&url=https://www.explainx.ai&hashtags=xai,explainable_ai,explainable_machine_learning,trust_in_ai,transparent_ai)

<img width="1000" src="rf_starter_example.png" alt="explainX.ai">

#### Why we need explainability & interpretibility?
ExplainX.ai is a model explainability/interpretability framework for data scientists and business users.

#### Why we need model explainability & interpretibility?

Essential for:
1. Model debugging - Why did my model make a mistake?
2. Detecting fairness issues - Is my model biased? If yes, where?
3. Human-AI cooperation - How can I understand and trust the model's decisions?
4. Regulatory compliance - Does my model satisfy legal & regulatory requirements?
5. High-risk applications - Healthcare, Financial Services, FinTech, Judicial, Security etc,.
1. Explaining model predictions
2. Debugging models
3. Detecting biases in data
4. Gaining trust of business users
5. Successfully deploying AI solution

Visit our website to learn more: https://www.explainx.ai
#### What questions can we answer with explainX?

## Try it out
1. Why did my model make a mistake?
2. Is my model biased? If yes, where?
3. How can I understand and trust the model's decisions?
4. Does my model satisfy legal & regulatory requirements?

* [Installing explainX](https://docs.explainx.ai/getting-started/installation)
* [Working Examples](https://docs.explainx.ai/getting-started/starter-example)
* [explainX Dashboard Features](https://docs.explainx.ai/tutorials/analyzing-dashboard)
* [Documentation](https://docs.explainx.ai/)
* [Help Us Improve explainX.ai](https://forms.gle/5Q1xaHd7s6UQkRzf8)
Visit our website to learn more: https://www.explainx.ai

# Get Started

# Installation
## Installation

Python 3.5+ | Linux, Mac, Windows (Install [Microsoft C++ Build Tools](https://visualstudio.microsoft.com/visual-cpp-build-tools/) first to run on Windows.)
Python 3.5+ | Linux, Mac, Windows

```sh
pip install explainx
```

To download on Windows, please install [Microsoft C++ Build Tools](https://visualstudio.microsoft.com/visual-cpp-build-tools/) first and then install the explainX package via ``` pip ```

## Installation on the cloud
If you are using AWS SageMaker of Jupyter Notebook deployed on the cloud, visit our documentation for step-by-step guide installing and running explainX. [Cloud Installation Instructions](https://docs.explainx.ai/getting-started/installation)
If you are using a notebook instance on the cloud (AWS SageMaker, Colab, Azure), please follow our step-by-step guide to install & run explainX cloud.
[Cloud Installation Instructions](https://docs.explainx.ai/getting-started/installation)


## Example Usage
## Usage (Example)
After successfully installing explainX, open up your Python IDE of Jupyter Notebook and simply follow the code below to use it:

1. Import required module.
Expand All @@ -57,7 +59,6 @@ After successfully installing explainX, open up your Python IDE of Jupyter Noteb
from explainx import *
from sklearn.ensemble import RandomForestClassifier
from sklearn.model_selection import train_test_split

```

2. Load and split your dataset into x_data and y_data
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<img width="1000" src="demo-explainx-with-sound.gif" alt="explainX.ai">

Learn to analyze the dashboard by following this link: [explainX Dashboard Features](https://explainx-documentation.netlify.app/analyze-dashboard/)

Visit the documentation to [learn more](https://docs.explainx.ai/)

## Models Supported
1. Catboost
2. xgboost==1.0.2
3. Gradient Boosting Regressor
4. RandomForest Model
5. SVM
6. KNeighboursClassifier
7. Logistic Regression
8. DecisionTreeClassifier
9. Scikit-learn Models
10. Neural Networks

## Walkthough Video Tutorial

Please click on the image below to load the tutorial:
Expand All @@ -133,6 +118,38 @@ Please click on the image below to load the tutorial:

(Note: Please manually set it to 720p or greater to have the text appear clearly)

## Supported Techniques

|Interpretability Technique | Status |
|--|--|
|SHAP Kernel Explainer| Live |
|SHAP Tree Explainer| Live |
|What-if Analysis| Live |
|Model Performance Comparison | Live |
|Partial Dependence Plot| Live |
|Surrogate Decision Tree | Coming Soon |
|Anchors | Coming Soon |
|Integrated Gradients (IG)| Coming Soon |

## Main Models Supported

| No. | Model Name | Status |
|--|--|--|
|1. | Catboost | Live|
|2. | XGboost==1.0.2 | Live|
|3. | Gradient Boosting Regressor| Live|
|4. | RandomForest Model| Live|
|5. | SVM|Live|
|6. | KNeighboursClassifier| Live
|7. | Logistic Regression| Live |
|8. | DecisionTreeClassifier|Live |
|9. | All Scikit-learn Models|Live |
|10.| Neural Networks|Live |
|11.| H2O.ai AutoML | Next in Line |
|12.| TensorFlow Models | Coming Soon |
|13.| PyTorch Models | Coming Soon |


## Contributing
Pull requests are welcome. In order to make changes to explainx, the ideal approach is to fork the repository then clone the fork locally.

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