jax-relax 0.2.8

JAX-based Recourse Explanation Library

ReLax

Overview | Installation | Tutorials | Documentation | Citing ReLax

Overview

ReLax (Recourse Explanation Library in Jax) is an efficient and scalable benchmarking library for recourse and counterfactual explanations, built on top of jax. By leveraging language primitives such as vectorization, parallelization, and just-in-time compilation in jax, ReLax offers massive speed improvements in generating individual (or local) explanations for predictions made by Machine Learning algorithms.

Some of the key features are as follows:

• 🏃 Fast and scalable recourse generation.

• 🚀 Accelerated over cpu, gpu, tpu.

• 🪓 Comprehensive set of recourse methods implemented for benchmarking.

• 👐 Customizable API to enable the building of entire modeling and interpretation pipelines for new recourse algorithms.

Installation

pip install jax-relax
# Or install the latest version of `jax-relax`
pip install git+https://github.com/BirkhoffG/jax-relax.git 

To futher unleash the power of accelerators (i.e., GPU/TPU), we suggest to first install this library via pip install jax-relax. Then, follow steps in the official install guidelines to install the right version for GPU or TPU.

Dive into ReLax

ReLax is a recourse explanation library for explaining (any) JAX-based ML models. We believe that it is important to give users flexibility to choose how to use ReLax. You can

• only use methods implemeted in ReLax (as a recourse methods library);
• build a pipeline using ReLax to define data module, training ML models, and generating CF explanation (for constructing recourse benchmarking pipeline).

ReLax as a Recourse Explanation Library

We introduce basic use cases of using methods in ReLax to generate recourse explanations. For more advanced usages of methods in ReLax, See this tutorials.

from relax.methods import VanillaCF
from relax import DataModule, MLModule, generate_cf_explanations, benchmark_cfs
from sklearn.datasets import make_classification
from sklearn.model_selection import train_test_split
import functools as ft
import jax

Let’s first generate synthetic data:

xs, ys = make_classification(n_samples=1000, n_features=10, random_state=42)
train_xs, test_xs, train_ys, test_ys = train_test_split(xs, ys, random_state=42)

Next, we fit an MLP model for this data. Note that this model can be any model implmented in JAX. We will use the MLModule in ReLax as an example.

model = MLModule()
model.train((train_xs, train_ys), epochs=10, batch_size=64)

Generating recourse explanations are straightforward. We can simply call generate_cf of an implemented recourse method to generate one recourse explanation:

vcf = VanillaCF(config={'n_steps': 1000, 'lr': 0.05})
cf = vcf.generate_cf(test_xs[0], model.pred_fn)
assert cf.shape == test_xs[0].shape

Or generate a bunch of recourse explanations with jax.vmap:

generate_fn = ft.partial(vcf.generate_cf, pred_fn=model.pred_fn)
cfs = jax.vmap(generate_fn)(test_xs)
assert cfs.shape == test_xs.shape

ReLax for Building Recourse Explanation Pipelines

The above example illustrates the usage of the decoupled relax.methods to generate recourse explanations. However, users are required to write boilerplate code for tasks such as data preprocessing, model training, and generating recourse explanations with feature constraints.

ReLax additionally offers a one-liner framework, streamlining the process and helping users in building a standardized pipeline for generating recourse explanations. You can write three lines of code to benchmark recourse explanations:

data_module = DataModule.from_numpy(xs, ys)
exps = generate_cf_explanations(vcf, data_module, model.pred_fn)
benchmark_cfs([exps])

See Getting Started with ReLax for an end-to-end example of using ReLax.

Supported Recourse Methods

ReLax currently provides implementations of 9 recourse explanation methods.

Method	Type	Paper Title	Ref
VanillaCF	Non-Parametric	Counterfactual Explanations without Opening the Black Box: Automated Decisions and the GDPR.	[1]
DiverseCF	Non-Parametric	Explaining Machine Learning Classifiers through Diverse Counterfactual Explanations.	[2]
ProtoCF	Semi-Parametric	Interpretable Counterfactual Explanations Guided by Prototypes.	[3]
CounterNet	Parametric	CounterNet: End-to-End Training of Prediction Aware Counterfactual Explanations.	[4]
GrowingSphere	Non-Parametric	Inverse Classification for Comparison-based Interpretability in Machine Learning.	[5]
CCHVAE	Semi-Parametric	Learning Model-Agnostic Counterfactual Explanations for Tabular Data.	[6]
VAECF	Parametric	Preserving Causal Constraints in Counterfactual Explanations for Machine Learning Classifiers.	[7]
CLUE	Semi-Parametric	Getting a CLUE: A Method for Explaining Uncertainty Estimates.	[8]
L2C	Parametric	Feature-based Learning for Diverse and Privacy-Preserving Counterfactual Explanations	[9]

Citing ReLax

To cite this repository:

@software{relax2023github,
  author = {Hangzhi Guo and Xinchang Xiong and Amulya Yadav},
  title = {{R}e{L}ax: Recourse Explanation Library in Jax},
  url = {http://github.com/birkhoffg/jax-relax},
  version = {0.2.0},
  year = {2023},
}