Intel-xai

New Features:
- ShapUI: a user interface to explore and compare impact scores of model predictions for each record of a tabular data set and discover insights of a model's behavior.
- Added info panel feature with text descriptions designed to help user with interpreting graphs
- Experimental support for `Python 3.10`

Validated configuration
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- Ubuntu 22.04 LTS
- Python 3.9, 3.10
- Intel® Optimization for TensorFlow 2.11.0
- PyTorch 1.13.1
- Torchvision 0.14.1
- TensorFlow Hub 0.12.0
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Known limitations
* Model Card Generator is only supported on Python 3.9 and did not get packaged as part of installer wheel

New Features:
- Single installer for both Model card generator and Explainers

**Explainers:**
- Unified Explainers' APIs
- CAM explainer which utilizes XGradCAM, the SOTA CAM method
- EigenCAM explainer for object detection model (FasterRCNN, YOLO)
- Compatibility support for Frozen models introduced by SciPy 1.10

Jupyter Notebooks
- ResNet50 ImageNet Classification using the CAM Explainer
- Custom CNN MNIST Classification using the Attributions Explainer
- Custom NN NewsGroups Classification using the Attributions Explainer
- Custom CNN CIFAR-10 Classification using the Attributions Explainer
- Multimodal Breast Cancer Detection Explainability
- Fine Tuned Text Classifier with PyTorch using the Intel® Explainable AI API
- Custom Neural Network Heart Disease Classification using the Attributions Explainer

Bug fixes:
- Many documentation improvements
- Improve test coverage for both Explainer and Model card generator-

Validated configuration
* Ubuntu 20.04 LTS
* Python 3.9
* Intel® Optimization for TensorFlow 2.11.0
* PyTorch 1.13.1
* Torchvision 0.14.1
* TensorFlow Hub 0.12.0

Known limitations
* Intel® Explainable AI Tools in only supported on Python 3.9

New Features:

**Model Card Generator:**
- Support for general model overview plots visualize performance as a function of threshold score.
- Support for interactive plots to visualize fairness metrics across data groupings.
- Added support for Model Card generation for PyTorch models.
- Added support for Model Cards for multiple datasets.

**Explainer:**
- Allows injection of XAI methods into Python workflows/notebooks without requiring version compatibility of resident packages in the active python environment.
- Supports 3 explainable plugin methods:
- feature attributions: Explains a model’s predictions based on how the model has weighted features it’s been trained on
- metrics: calculates and plots the standard base metrics used to evaluate model performance
- language model explanations: explains transformer based language models by visualizing input token importance, hidden state contributions, sequence embeddings and attention heads
- An interactive CLI allows the user to install each plugin. Provides a simple solution to create new plugins and expand on existing plugins.
- Complete documentation with notebooks examples in the natural language, computer vision, and data frame domain.

Bug fixes:

**Model Card Generator:**
- N/A

**Explainer:**
- N/A, Initial public release

Supported Configurations

**Intel® Explainable AI Tools v0.2.0 is validated on the following environment:**

* Ubuntu 20.04 LTS
* Python 3.9

Supported Frameworks
**- TensorFlow**

New features

- **Model Card Generator:**
> Allows users to create interactive HTML reports of containing model performance and fairness metrics.
> Supports general model overview plots visualize performance as a function of threshold score.
> Supports interactive plots to visualize fairness metrics across data groupings.

Bug fixes:
- N/A

Supported Configurations
**Intel® Explainable AI Tools v0.0.1 is validated on the following environment:**

* Ubuntu 20.04 LTS
* Python 3.8, 3.9