Sliceline

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, 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:

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 for a more thorough tutorial:

Implementing Sliceline on Titanic dataset
Implementing Sliceline on California housing dataset

🛠 Installation

Sliceline is intended to work with Python 3.7 or above. Installation can be done with pip:

pip install sliceline

There are wheels available 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:

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

Or, through SSH:

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

🔗 Useful links

👐 Contributing

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

Open a discussion 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 if you think you’ve spotted a bug or a performance issue.

Please check out the contribution guidelines 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.

Slicefinder

class sliceline.Slicefinder(alpha: float = 0.6, k: int = 1, max_l: int = 4, min_sup: int = 10, verbose: bool = True)

Slicefinder class.

SliceLine is a fast, linear-algebra-based slice finding for ML Model Debugging.

Given an input dataset (X) and a model error vector (errors), SliceLine finds the k slices in X that identify where the model performs significantly worse. A slice is a subspace of X defined by one or more predicates. The maximal dimension of this subspace is controlled by max_l.

The slice scoring function is the linear combination of two objectives:

Find sufficiently large slices, with more than min_sup elements (high impact on the overall model)
With substantial errors (high negative impact on sub-group/model)

The importance of each objective is controlled through a single parameter alpha.

Slice enumeration and pruning techniques are done via sparse linear algebra.

alpha: float, default=0.6: Weight parameter for the importance of the average slice error. 0 < alpha <= 1.
k: int, default=1: Maximum number of slices to return. Note: in case of equality between k-th slice score and the following ones, all those slices are returned, leading to _n_features_out slices returned. (_n_features_out >= k)
max_l: int, default=4: Maximum lattice level. In other words: the maximum number of predicate to define a slice.
min_sup: int, default=10: Minimum support threshold. Inspired by frequent itemset mining, it ensures statistical significance.
verbose: bool, default=True: Controls the verbosity.

top_slices_: np.ndarray of shape (_n_features_out, number of columns of the input dataset): The _n_features_out slices with the highest score. None values in slices represent unused column in the slice.
average_error_: float: Mean value of the input error.

SliceLine: Fast, Linear-Algebra-based Slice Finding for ML Model Debugging, from Svetlana Sagadeeva and Matthias Boehm of Graz University of Technology.