Introduction to Interpret Community
The Interpret Community SDK is an innovative tool designed to enhance the understanding and transparency of machine learning models, specifically focusing on those trained with tabular data. Building on the foundation laid by the original Interpret project, Interpret-Community introduces additional experimental techniques and utilities to address real-world data challenges and workflows.
Overview of Interpret-Community
Interpret-Community enriches the capabilities of Interpret by adding new experimental interpretability techniques. It is particularly beneficial for data scientists and researchers who need to apply these techniques to practical, large-scale datasets.
Key features include:
- Expansion of Techniques: Incorporates experimental methods for deeper insights, allowing further innovation and experimentation.
- Scalability: Optimized to run efficiently even on large datasets.
- Feature Engineering Insights: Offers the ability to reverse-engineer feature pipelines, translating insights back to the original data features.
- Interactive Visualizations: Provides powerful visualization tools to help teams explore and understand their data in meaningful ways.
Installation
Getting started with Interpret-Community is simple. It can be installed via PyPI using the following command:
pip install interpret-community
For detailed installation instructions, visit the documentation page.
Supported Models
This SDK supports a wide variety of models, including those created using Python libraries such as numpy, pandas, and scipy. It's compatible with different machine learning frameworks like PyTorch, TensorFlow, and Keras, and it can handle both models and prediction pipelines that conform to the Scikit-learn predict or predict_proba function signature.
Supported Interpretability Techniques
Interpret-Community offers a range of explainers, each serving a unique purpose:
- SHAP Kernel Explainer: Provides model-agnostic SHAP value estimates.
- GPU SHAP Kernel Explainer: Accelerates SHAP Kernel computations using GPU, suitable for both GPU and CPU models.
- SHAP Tree Explainer: Designed for tree-based models, offering efficient SHAP calculations.
- SHAP Deep Explainer: High-speed SHAP approximation for deep learning models.
- SHAP Linear Explainer: Targets linear models with considerations for feature interactions.
- Mimic Explainer: Utilizes interpretable surrogate models to mimic and explain black box model behavior.
- Permutation Feature Importance Explainer (PFI): Based on feature shuffling, evaluates feature significance globally.
- LIME Explainer: Provides local, interpretable explanations via linear approximations.
Additionally, the TabularExplainer dynamically selects the appropriate SHAP explainer based on the model's type.
Using Interpret-Community
To use these explainers, begin by constructing an explainer with your model or prediction function:
from interpret.ext.blackbox import TabularExplainer
explainer = TabularExplainer(model, x_train, features=breast_cancer_data.feature_names, classes=classes)
You can generate both global and local explanations:
- Global Explanation: Understand overall model behavior.
- Local Explanation: Explore predictions for specific instances.
Visualizations
Enhance your understanding of model behavior and data insights using the Explanation Dashboard:
pip install raiwidgets
This dashboard provides various visualization options, such as:
- Model Performance: Explore model accuracy and other metrics.
- Data Explorer: Investigate dataset details.
- Feature Importance: Understand both aggregate and individual feature impacts.
Contributing and Community
Interpret-Community welcomes contributions and collaboration. Contributors must adhere to the Developer Certificate of Origin (DCO) guidelines. To learn more about contributing, follow the guidelines provided by the project's DCO process.
Maintaining Ethical Standards
The project adheres to the GitHub Community Guidelines, ensuring respectful and constructive interactions among participants.
For any security issues or concerns, please contact the Microsoft Security Response Center as specified.
By bringing transparency to complex models and providing robust interpretability tools, Interpret-Community seeks to foster a deeper understanding of machine learning models in practical applications.