The goal of this repository is to discover regions of the input space with reduced feature interactions. These regions are idenfied as the leaves of a binary decision tree that is trained to minimize feature interactions As a result, post-hoc explainers such as PDP and SHAP increase in agreement when restricted to each region.
To create the conda environment run
conda env create --file environment.yml
conda activate FDTreesThe code relies on a C++ implementation of the Interventional TreeSHAP algorithm to efficiently compute Shapley Values, Shapley Taylor Indices, and the H tensor from the paper. To compile the C++ code, run
python3 setup.py buildIf everything worked well, you should see a .so file in a new build directory.
All experiments are done in the experiments directory
cd experimentsThe script that start with 0_* are toy experiments meant to illustrate how FD-Trees work.
These script can be run directly without providing arguments.
0_0_motivation.pyThe first toy example in the paper (the piece-wise linear function with two regions)0_1_illustration.pyThe code to reproduce Figure 2.0_2_interactions.pyA 2D example where we visualize interactions.0_3_correlations.pyA simple example where we investigate correlated features.0_4_gadget_pdp.pyToy example to convey the intuition behind GADGET-PDP.
The remaining script are numbered 1_* (model training), 2_* (interaction detection),
3_* (regional explanations computations), and 4_* (plot results).
The reproduce our results, run the following bash scripts
Model Training ./script_train.sh
Regional Explanations ./script_explain.sh <seed> with <seed> taking values 0, 1, 2, 3, 4.
Stability of the Partitions ./script_stability.sh
Finally, the results of the paper are plotted via
4_0_plot_california.pyPlot the figures from Section D.3.4.4_1_plot_disagreements.pyPlot Figure 3 and Table 2 showing the explanation disagreements.4_2_partition_stability.pyPlot the Figures in Section D.1 regarding the stability of the partitions w.r.t the subsample size.