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https://github.com/explainX/explainx
Explainable AI framework for data scientists. Explain & debug any blackbox machine learning model with a single line of code. We are looking for co-authors to take this project forward. Reach out @ [email protected]
https://github.com/explainX/explainx
aws-sagemaker bias blackbox explainability explainable-ai explainable-artificial-intelligence explainable-ml explainx interpretability interpretable-ai interpretable-machine-learning machine-learning machine-learning-interpretability scikit-learn transparency xai
Last synced: 20 days ago
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Explainable AI framework for data scientists. Explain & debug any blackbox machine learning model with a single line of code. We are looking for co-authors to take this project forward. Reach out @ [email protected]
- Host: GitHub
- URL: https://github.com/explainX/explainx
- Owner: explainX
- License: mit
- Created: 2020-06-16T14:27:15.000Z (almost 4 years ago)
- Default Branch: master
- Last Pushed: 2024-03-01T15:54:36.000Z (4 months ago)
- Last Synced: 2024-05-14T14:41:45.036Z (about 1 month ago)
- Topics: aws-sagemaker, bias, blackbox, explainability, explainable-ai, explainable-artificial-intelligence, explainable-ml, explainx, interpretability, interpretable-ai, interpretable-machine-learning, machine-learning, machine-learning-interpretability, scikit-learn, transparency, xai
- Language: Jupyter Notebook
- Homepage: https://www.explainx.ai
- Size: 61.2 MB
- Stars: 393
- Watchers: 10
- Forks: 54
- Open Issues: 6
-
Metadata Files:
- Readme: README.md
- Funding: .github/FUNDING.yml
- License: LICENSE
Lists
- awesome-machine-learning-interpretability - explainX
- Awesome-explainable-AI - https://github.com/explainX/explainx
- awesome-meteo - explainx
- awesome-artificial-intelligence - ExplainX - ExplainX is a fast, light-weight, and scalable explainable AI framework for data scientists to explain any black-box model to business stakeholders. (Code)
- awesome-stars - explainX/explainx - Explainable AI framework for data scientists. Explain & debug any blackbox machine learning model with a single line of code. We are looking for co-authors to take this project forward. Reach out @ ms (Jupyter Notebook)
- awesome-python-machine-learning-resources - GitHub - 34% open · ⏱️ 02.02.2021): (模型的可解释性)
- awesome-artificial-intelligence - ExplainX - ExplainX is a fast, light-weight, and scalable explainable AI framework for data scientists to explain any black-box model to business stakeholders. (Code)
- awesome-artificial-intelligence - ExplainX - ExplainX is a fast, lightweight, and scalable explainable AI framework for data scientists to explain any black-box model to business stakeholders. (Code)
README
# explainX: Explainable AI Framework for Data Scientists
### We are looking for co-authors to take this project forward. Reach out @ [email protected]
#### ExplainX is a model explainability/interpretability framework for data scientists and business users.
[](https://pypi.org/project/explainx/)
[](https://pepy.tech/project/explainx)
[](https://www.explainx.ai/)
Use explainX to understand overall model behavior, explain the "why" behind model predictions, remove biases and create convincing explanations for your business stakeholders. [](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)
#### Why we need model explainability & interpretibility?
Essential for:
1. Explaining model predictions
2. Debugging models
3. Detecting biases in data
4. Gaining trust of business users
5. Successfully deploying AI solution
#### What questions can we answer with explainX?
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?
#### We have deployed the app on our server so you can play around with the dashboard. Check it out:
Dashboard Demo: http://3.128.188.55:8080/
# Getting Started
## Installation
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 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)
## 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.
```python
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
```python
#Load Dataset: X_Data, Y_Data
#X_Data = Pandas DataFrame
#Y_Data = Numpy Array or List
X_data,Y_data = explainx.dataset_heloc()
```
3. Split dataset into training & testing.
``` python
X_train, X_test, Y_train, Y_test = train_test_split(X_data,Y_data, test_size=0.3, random_state=0)
```
4. Train your model.
```python
# Train a RandomForest Model
model = RandomForestClassifier()
model.fit(X_train, Y_train)
```
After you're done training the model, you can either access the complete explainability dashboard or access individual techniques.
## Complete Explainability Dashboard
To access the entire dashboard with all the explainability techniques under one roof, follow the code down below. It is great for sharing your work with your peers and managers in an interactive and easy to understand way.
5.1. Pass your model and dataset into the explainX function:
```python
explainx.ai(X_test, Y_test, model, model_name="randomforest")
```
5.2. Click on the dashboard link to start exploring model behavior:
```python
App running on https://127.0.0.1:8080
```
## Explainability Modules
In this latest release, we have also given the option to use explainability techniques individually. This will allow the user to choose technique that fits their personal AI use case.
6.1. Pass your model, X_Data and Y_Data into the explainx_modules function.
```python
explainx_modules.ai(model, X_test, Y_test)
```
As an upgrade, we have eliminated the need to pass in the model name as explainX is smart enough to identify the model type and problem type i.e. classification or regression, by itself.
You can access multiple modules:
Module 1: Dataframe with Predictions
```python
explainx_modules.dataframe_graphing()
```
Module 2: Model Metrics
```python
explainx_modules.metrics()
```
Module 3: Global Level SHAP Values
```python
explainx_modules.shap_df()
```
Module 4: What-If Scenario Analysis (Local Level Explanations)
```python
explainx_modules.what_if_analysis()
```
Module 5: Partial Dependence Plot & Summary Plot
```python
explainx_modules.feature_interactions()
```
Module 6: Model Performance Comparison (Cohort Analysis)
```python
explainx_modules.cohort_analysis()
```
To access the modules within your jupyter notebook as IFrames, just pass the mode='inline' argument.
For detailed description into each module, check out our documentation at https://www.docs.explainx.ai
## Cloud Installation
**If you are running explainX on the cloud e.g., AWS Sagemaker?** **https://0.0.0.0:8080** will not work.
Please visit our documentation for installation instructions for the cloud: [Cloud Installation Instructions](https://docs.explainx.ai/getting-started/installation)
After installation is complete, just open your **terminal** and run the following command.
```jupyter
lt -h "https://serverless.social" -p [port number]
```
```jupyter
lt -h "https://serverless.social" -p 8080
```
## Walkthough Video Tutorial
Please click on the image below to load the tutorial:
[](https://youtu.be/CDMpOismME8)
(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 | Live |
|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.
For major changes, please open an issue first to discuss what you would like to change.
Please make sure to update tests as appropriate.
## Report Issues
Please help us by [reporting any issues](https://github.com/explainX/explainx/issues/new) you may have while using explainX.
## License
[MIT](https://choosealicense.com/licenses/mit/)