Sliceline is a Python library for fast slice finding for Machine Learning model debugging.

It is an implementation of SliceLine: Fast, Linear-Algebra-based Slice Finding for ML Model Debugging <https://mboehm7.github.io/resources/sigmod2021b_sliceline.pdf>__, from Svetlana Sagadeeva and Matthias Boehm of Graz University of Technology.

👉 Getting started

Given an input dataset X and a model error vector errors, SliceLine finds the top slices in X that identify where a ML model performs significantly worse.

You can use sliceline as follows:

.. code:: python

from sliceline.slicefinder import Slicefinder

slice_finder = Slicefinder()

slice_finder.fit(X, errors)

print(slice_finder.top_slices_)

X_trans = slice_finder.transform(X)

We invite you to check the demo notebooks <https://github.com/DataDome/sliceline/blob/main/notebooks>__ for a more thorough tutorial:

1. Implementing Sliceline on Titanic dataset
2. Implementing Sliceline on California housing dataset

🛠 Installation

Sliceline is intended to work with Python 3.10 or above. Installation can be done with pip:

.. code:: sh

pip install sliceline

There are wheels available <https://pypi.org/project/sliceline/#files>__ for Linux, MacOS, and Windows, which means that you most probably won’t have to build Sliceline from source.

You can install the latest development version from GitHub as so:

.. code:: sh

pip install git+https://github.com/DataDome/sliceline --upgrade

Or, through SSH:

.. code:: sh

pip install git+ssh://git@github.com/datadome/sliceline.git --upgrade

⚡ Performance Optimization

Sliceline includes optional Numba JIT compilation for 5-50x performance improvements on scoring operations.

Quick Installation:

.. code:: sh

With optimization support

pip install sliceline[optimized]

Benefits:

• 5-6x faster scoring operations
• 1.4-4.5x faster overall fit() performance
• Up to 17% memory reduction on large datasets
• Automatic fallback to pure NumPy if Numba not available

System Requirements:

Numba requires LLVM to be installed:

.. code:: sh

macOS

brew install llvm

Linux (Ubuntu/Debian)

sudo apt-get install llvm

Disabling Numba:

If you need to disable Numba JIT (e.g., in restricted environments), set the environment variable:

.. code:: sh

export NUMBA_DISABLE_JIT=1

Docker / Read-only Filesystems:

Numba requires a writable cache directory. In Docker containers or read-only filesystems, set NUMBA_CACHE_DIR to a writable path:

.. code:: dockerfile

ENV NUMBA_CACHE_DIR=/tmp/numba_cache

If the cache directory is not writable, Sliceline will automatically fall back to pure NumPy.

Verify Optimization:

.. code:: python

from sliceline import is_numba_available

print("Numba available:", is_numba_available())

See the performance benchmarks <https://github.com/DataDome/sliceline/tree/main/benchmarks>__ for detailed metrics.

🔗 Useful links

• Documentation <https://sliceline.readthedocs.io/en/stable/>__
• Package releases <https://pypi.org/project/sliceline/#history>__
• SliceLine paper <https://mboehm7.github.io/resources/sigmod2021b_sliceline.pdf>__

👐 Contributing

Feel free to contribute in any way you like, we’re always open to new ideas and approaches.

• Open a discussion <https://github.com/DataDome/sliceline/discussions/new>__ if you have any question or enquiry whatsoever. It’s more useful to ask your question in public rather than sending us a private email. It’s also encouraged to open a discussion before contributing, so that everyone is aligned and unnecessary work is avoided.
• Feel welcome to open an issue <https://github.com/DataDome/sliceline/issues/new/choose>__ if you think you’ve spotted a bug or a performance issue.

Please check out the contribution guidelines <https://github.com/DataDome/sliceline/blob/main/CONTRIBUTING.md>__ if you want to bring modifications to the code base.

📝 License

Sliceline is free and open-source software licensed under the 3-clause BSD license <https://github.com/DataDome/sliceline/blob/main/LICENSE>__.