XDual

A set of Python scripts for explaining boosted tree models by computing either abductive or contrastive explanations (or both), based on the hitting set duality between the two concepts. The implementation targets tree ensembles trained with XGBoost and supports computing and enumerating subset- and cardinality-minimal rigorous explanations.

Getting Started

Before using XDual, make sure you have the following Python packages installed:

• anytree
• numpy
• pandas
• pySMT
• PySAT
• scikit-learn
• XGBoost
• ortools

Please, follow the installation instructions on these projects' websites to install them properly. (If you spot any other package dependency not listed here, please, let us know.)

Usage

XDual has a number of parameters, which can be set from the command line. To see the list of options, run:

$ xdual.py -h

Preparing a dataset

XDual can be used with datasets in the CSV format. If a dataset contains continuous data, you can use XDual straight away (with no option -c specified). Otherwise, you need to process the categorical features of the dataset. For this, you need to do a few steps:

1. Assume your dataset is stored in file somepath/dataset.csv.

2. Create another file named somepath/dataset.csv.catcol that contains the indices of the categorical columns of somepath/dataset.csv. For instance, if columns 0, 1, and 5 contain categorical data, the file should contain the lines

   0
   1
   5
   

3. Now, the following command:

$ xdual.py -p --pfiles dataset.csv,somename somepath/

creates a new file somepath/somename_data.csv with the categorical features properly handled. As an example, you may want to check the command on the benchmark datasets, e.g.

$ xdual.py -p --pfiles compas.csv,compas bench/fairml/compas/

Training a tree ensemble

Before extracting explanations, an XGBoost model must be trained:

$ xdual.py -c -t -n 50 bench/fairml/compas/compas_data.csv

Here, 50 trees per class are trained. Also, parameter -c is used because the data is categorical. By default, the trained model is saved in the file temp/compas_data/compas_data_nbestim_50_maxdepth_3_testsplit_0.2.mod.pkl.

Computing an abductive explanation

Assuming that one targets explaining a data instance 5,0,0,0,0,0,0,0,0,0,0, a rigorous abductive explanation for such an instance can be computed by running the following command:

$ xdual.py -c -e smt -s z3 -x '5,0,0,0,0,0,0,0,0,0,0' -v temp/compas_data/compas_data_nbestim_50_maxdepth_3_testsplit_0.2.mod.pkl

Here, parameter -e specifies the model encoding (SMT) while parameter -s identifies an SMT solver to use (various SMT solvers can be installed in pySMT - here we use Z3). This command computes a subset-minimal explanation, i.e. it is guaranteed that no proper subset of the reported explanation can serve as an explanation for the given prediction.

Alternatively, a cardinality-minimal (i.e. smallest size) explanation can be computed by specifying the -M option additionally:

$ xdual.py -c -e smt -M -s z3 -x '5,0,0,0,0,0,0,0,0,0,0' -v temp/compas_data/compas_data_nbestim_50_maxdepth_3_testsplit_0.2.mod.pkl

Computing a contrastive explanation

Similarly, one can compute either subset- or cardinality-minimal rigorous contrastive explanation. For example, a rigorous contrastive explanation for the same data instance can be computed by running the following command:

$ xdual.py -c -e smt -s z3 -x '5,0,0,0,0,0,0,0,0,0,0' --xtype contrastive -v temp/compas_data/compas_data_nbestim_50_maxdepth_3_testsplit_0.2.mod.pkl

Alternatively, a cardinality-minimal contrastive explanation can be computed by specifying the -M option additionally:

$ xdual.py -c -e smt -M -s z3 -x '5,0,0,0,0,0,0,0,0,0,0' --xtype contrastive  -v temp/compas_data/compas_data_nbestim_50_maxdepth_3_testsplit_0.2.mod.pkl

Enumeration of explanations

XDual also supports enumeration of either abductive or contrastive explanations, or both. The tool can be instructed to enumerate explanations by specifying the number of explanations to compute, which can be done using the -N option:

$ ./xdual.py -N 2 -c -e smt -M -s z3 -x '5,0,0,0,0,0,0,0,0,0,0' --xtype contrastive  -v temp/compas_data/compas_data_nbestim_50_maxdepth_3_testsplit_0.2.mod.pkl

Running this command would compute two contrastive explanations. If instead of an integer, a user puts -N all, XDual will enumerate all contrastive explanations for a given data instance.

The same command-line option should be used for enumerating abductive explanations. Note that for abductive explanations, the value of option --xtype should be set to abductive (or unset).

Combinations of options can be used to choose the explanation enumeration algorithm and its additional parameters, including options -M, -u,--use-cld, and --use-mhs. (For the combination used in the experiment described in the paper, pleasee, see the experiment directory.)

Reproducing experimental results

Although it seems unlikely that the experimental results reported in the paper can be reproduced (due to randomization used in the training phase), similar results can be obtained if the following commands are executed:

$ cd experiment/
$ ./train-all.sh && ./extract-samples.sh
$ ./enumerate-all.sh

The final command should run the experiment the way it was set up for the paper. (Note that this will take a while.) The result files will contain the necessary statistics.

Reproducing CXp's enumeration experimental results

Please prepare data and training a tree ensemble.

$ ./cxp_prepare.sh

To obtain images used in the paper:

$ ./cxp_enumeration.sh

This script will create a folder "image_temp" with four sub-folders: "real6", "fake6", "digit3", and "digit5" to reproduce results.

To run the correlation experiment:

$ ./run_intersect.py

It will create a folder image_temp with 100 sub-folders for each experiment.

[`real vs fake']. We create two groups of folders "digit_id_gan_first_x" and "digit_id_gan_last_x", x = 1..50, with images for the first and the second class, respectively. The correlation results are in each folder (results_exp.txt, line "Percentage: 0.6764705882352942") To see correlations:

$ for file in  image_temp/digit_id_partition*/*txt; do grep -H Percentage  "${file}" | tail -n 1;done

[3 vs 5 digits'] Also, we create two groups of folders "digit_id_partition_first_x" and "digit_id_partition_last_x", x = 1..50, with images for the first and the second class, respectively. The correlation results are in each folder (results_exp.txt):

To see correlations:

$ for file in  image_temp/digit_id_gan*/*txt; do grep -H Percentage  "${file}" | tail -n 1;done

License

This project is licensed under the MIT License - see the LICENSE file for details.