Explabox

With the explabox, you can turn your ingestibles (model and data) into digestibles (statistics and insights). You no longer need a whole suite of Python packages for descriptive statistiscs, explainable AI (XAI), and safety, robustness & fairness testing. Simply pip install explabox, and you are good to go:

from explabox import Explabox, import_data, import_model

# Import model and data
data = import_data('data.csv', data_cols='review', label_cols='score')
model = import_model('classifier.onnx')

# Wrap in the explabox
box = Explabox(data=data, model=model)

# Explore, examine, explain and expose!

Once your data (e.g. .csv, HDF5, pandas DataFrame or huggingface Dataset) and model (e.g. scikit-learn, PyTorch or tensorflow) are wrapped in the explabox, you are good to go!

The explabox can be used to:

• Explore: describe aspects of the model and data.
• Examine: calculate quantitative metrics on how the model performs.
• Expose: see model sensitivity to random inputs (safety), test model generalizability (robustness), and see the effect of adjustments of attributes in the inputs (e.g. swapping male pronouns for female pronouns; fairness), for the dataset as a whole (global) as well as for individual instances (local).
• Explain: use XAI methods for explaining the whole dataset (global), model behavior on the dataset (global), and specific predictions/decisions (local).

# Explore descriptive statistics
box.explore()
...

# Examine model performance on the data
box.examine.wrongly_classified()
...

# Explain local/global model behavior
box.explain.explain_prediction('Explain this instance!')
...

# Expose where the model is sensitive to
box.expose.compare_metrics(perturbation='upper')
...

The explabox aims to support data scientists and ML engineers, but also makes it easy to document and communicate it with your stakeholders! It was designed and instigated in a professional environment, where the explabox is used to support the AI lifecycle with traceable, accountable & transparent model development and usage.

Curious as to how the explabox can support your workflow? To get started, check out the documentation or the GitHub page!