xiemeigongzi / generative-causal-explanations Goto Github PK

Code for "Generative causal explanations of black-box classifiers" by Matt O'Shaughnessy, Greg Canal, Marissa Connor, Mark Davenport, and Chris Rozell (Proc. NeurIPS 2020).

Demo/quick start

demo.py contains code demonstrating the use of the generative causal explainer (GCE) class (GCE.py) and explanation plotting functions (plotting.py). The demo code reproduces Figure 3 (creating an explanation of a simple pretrained MNIST 3/8 classifier) and is the easiest place to start.

Reproduce main paper plots

Prerequisites Generating results requires only python (version 3.8.5 used), pytorch (version 1.6.0 with CUDA 10.1 used), numpy (version 1.19.1 used), scipy (version 1.5.2 used), and matplotlib (version 3.3.1 used). Using pretrained models requires CUDA, but you should be able to regenerate the results yourself without. Matlab and inkscape were used to create some final results.

Figure 3 (global explanations of MNIST digits) Run make_fig3.py. By default, this script will load a pretrained classifier from pretrained_models/mnist_38_classifier/ and a pretrained explanatory VAE from pretrained_models/mnist_38_gce/.

• To retrain the classifier: run train_mnist_classifier.py, setting dataset = 'mnist' and class_use = np.array([3,8]).
• To retrain the explanatory VAE: set retrain_gce = True, optionally changing any of the parameters at the top of the file. You may also want to set save_gce = True and change gce_path.

Figure 4 (comparison of glabal explanations with other methods)

• Left panel (local explanations from other popular techniques)
  • Run make_fig4_left.py. Note: this script requires tensorflow, keras, and scikit-image. In addition, it requires the LIME and SHAP packages.
• Right side of plot (our local explanation) Run make_fig4_right.py. By default, the script will load a pretrained classifier from pretrained_models/mnist_38_classifier/, load the corresponding pretrained explanatory VAE from pretrained_models/mist_38_gce/, and generate plots showing global explanations.
  • To retrain the classifier: The default pretrained classifier was created from train_mnist_classifier.py with dataset = 'mnist' and class_use = np.array([3,8]). To use a different classifier, change classifier_path at the top of the file.
  • To retrain the explanatory VAE: the default pretrained VAE was created from make_fig3.py (be sure to uncomment the lines in this file that save the GCE object if you make changes). To use a different classifier, change gce_path at the top of the make_fig4_right.py.

Figure 5 (quantitative results with fashion MNIST)

• Subfigures (a-b) (information flow and reduction in classifier accuracy for fashion MNIST classes 0/3/4) Run make_fig5ab.py. By default, the script will load a pretrained classifier from pretrained_models/fmnist_034_classifier/, load the corresponding pretrained explanatory VAE from pretrained_models/fmnist_034_gce/, and compute and plot figures 5(a-b).
  • To retrain the classifier: The default pretrained classifier was created from train_mnist_classifier.py with dataset = 'fmnist' and class_use = np.array([0,3,4]). To use a different classifier, change classifier_path at the top of the file.
  • To retrain the explanatory VAE: set retrain_gce = True, optionally changing any of the parameters at the top of the file. You may also want to set save_gce = True and change gce_path.
• Subfigures (c-d) (global explanation of fashion MNIST classes 0/3/4) Run make_fig5cd.py. By default, the script will load a pretrained classifier from pretrained_models/fmnist_034_classifier/, load the corresponding pretrained explanatory VAE from pretrained_models/fmnist_034_gce/, and create latent plot sweeps such as those in Figure 5(c-d) for each latent factor.
  • To retrain the classifier: The default pretrained classifier was created from train_mnist_classifier.py with dataset = 'fmnist' and class_use = np.array([0,3,4]). To use a different classifier, change classifier_path at the top of the file.
  • To retrain the explanatory VAE: set retrain_gce = True, optionally changing any of the parameters at the top of the file. You may also want to set save_gce = True and change gce_path.

Reproduce appendix plots

Figure 8 (empirical results for causal objective with linear/gaussian generative map, linear classifier)

• Run make_fig8.py. This will generate some rough plots and save results to results/fig8.mat.
• To create plots in paper: run first cell of make_fig8_fig9_fig10.m.

Figures 9-10 (empirical results for causal/combined objective with linear/gaussian generative map, AND classifier)

• Run make_fig9_fig10.py. This will generate some rough plots and save results to results/fig9.mat.
• To create plots in paper: run second and third cells of make_fig8_fig9_fig10.m.

Figure 11 (snapshots of parameter tuning procedure for MNIST 3/8) Run make_fig11.m. This script uses pre-saved results in results/tuning_mnist38_*.mat. These .mat files contain additional information from the parameter turning process shown.

Figure 12 (additional results for global explanations of MNIST digits): Contains complete results from Figure 3; see make_fig3.py.

Figure 13 (global explanations of 1/4/9 MNIST digits): Run make_fig13.py. By default, this script will load a pretrained classifier from pretrained_models/mnist_149_classifier/ and a pretrained explanatory VAE from pretrained_models/mnist_149_gce/.

• To retrain the classifier: run train_mnist_classifier.py, setting dataset = 'mnist' and class_use = np.array([1,4,9]).
• To retrain the explanatory VAE: set retrain_gce = True, optionally changing any of the parameters at the top of the file. You may also want to set save_gce = True and change gce_path.

Figure 14 (zoomed global explanations of 1/4/9 MNIST digits): Run make_fig14.py. By default, this script will load a pretrained classifier from pretrained_models/mnist_149_classifier/ and a pretrained explanatory VAE from pretrained_models/mnist_149_gce/.

• To retrain the classifier: run train_mnist_classifier.py, setting dataset = 'mnist' and class_use = np.array([1,4,9]).
• To retrain the explanatory VAE: set retrain_gce = True, optionally changing any of the parameters at the top of the file. You may also want to set save_gce = True and change gce_path.

Figure 15 (detailed plots of our method as used in local comparison) Contains complete results from Figure 4 (right); see make_fig4.py.

Figure 16 (snapshots of parameter tuning procedure for 0/3/4 fashion MNIST digits) Run make_fig16.m. This script uses pre-saved results in results/tuning_fmnist034_*.mat. These .mat files contain additional information from the parameter turning process shown.

Figure 17 (detailed global explanations of fashion MNIST) Contains complete results from Figure 5(c-d); see make_fig5cd.py.

Figure 18 (experiments comparing VAE capacity)

• Run make_fig18_fig19.py, which creates the file results/fig18.mat.
• Run the matlab script make_fig18.m to create the final plot. Note that this script requires the cbrewer matlab package.

Figure 19 (qualitative results for varying VAE capacity) Contains qualitative results from Figure 18; see make_fig18_fig19.py, which creates the files ./figs/fig19/XXfilters_lambdaYYY*.