vanderschaarlab/CARs

This repository contains the implementation of Concept Activation Regions, a new framework to explain deep neural networks with human concepts. For more details, please read our NeurIPS 2022 paper: 'Concept Activation Regions: a Generalized Framework for Concept-Based Explanations.

Concept Activation Regions

Code Author: Jonathan Crabbé (jc2133@cam.ac.uk)

This repository contains the implementation of CARs, a framework to explain the latent representations of neural networks with the help of user-defined concepts. For more details, please read our NeurIPS 2022 paper: 'Concept Activation Regions: A Generalized Framework For Concept-Based Explanations'.

1. Installation

From repository:

1. Clone the repository
2. Create a new virtual environment with Python 3.9
3. Run the following command from the repository folder:

pip install -r requirements.txt

When the packages are installed, you are ready to perform concept explanations.

OR from to install from source, simply perform:

pip install .

2. Toy example

Bellow, you can find a toy demonstration where we compute label-free feature and example importance with a MNIST autoencoder. The relevant code can be found in the folder explanations.

import torch
from torchvision.datasets import MNIST
from torchvision.transforms import transforms
from pathlib import Path
from cars.utils.dataset import generate_mnist_concept_dataset
from cars.models.mnist import ClassifierMnist
from cars.experiments.mnist import concept_to_class
from cars.explanations.concept import CAR
from cars.explanations.feature import CARFeatureImportance

device = torch.device("cuda") if torch.cuda.is_available() else torch.device("cpu")
data_dir = Path.cwd()/"data/mnist"

# Load model
model = ClassifierMnist(latent_dim=42).to(device)

# Load concept sets
X_train, C_train = generate_mnist_concept_dataset(concept_to_class["Loop"],
                                               data_dir,
                                               train=True,
                                               subset_size=200, 
                                               random_seed=42)
X_test, C_test = generate_mnist_concept_dataset(concept_to_class["Loop"],
                                               data_dir,
                                               train=False,
                                               subset_size=100, 
                                               random_seed=42)

# Evaluate latent representation
X_train = torch.from_numpy(X_train).to(device)
X_test = torch.from_numpy(X_test).to(device)
H_train = model.input_to_representation(X_train)
H_test = model.input_to_representation(X_test)

# Fit CAR classifier
car = CAR(device)
car.fit(H_train.detach().cpu().numpy(), C_train)
car.tune_kernel_width(H_train.detach().cpu().numpy(), C_train)

# Predict concept labels
C_predict = car.predict(H_test.detach().cpu().numpy())

# Load test set
test_set = MNIST(data_dir, train=False, download=True)
test_set.transform = transforms.Compose([transforms.ToTensor()])
test_loader = torch.utils.data.DataLoader(test_set, batch_size=100, shuffle=False)

# Evaluate feature importance on the test set
baselines = torch.zeros([1, 1, 28, 28]) # Black image baseline
car_features = CARFeatureImportance("Integrated Gradient", car, model, device)
feature_importance = car_features.attribute(test_loader, baselines=baselines)

3. Reproducing the paper results

MNIST Dataset

Run the following script

python -m experiments.mnist --name experiment_name --train --plot

where the --train option should only be used one time to fit a model for all the experiments. The experiment_name parameter can take the following values:

experiment_name	description
concept_accuracy	Consistency check for CAR classifiers (paper Section 3.1.1)
statistical_significance	Statistical significance for the concepts (paper Section 3.1.1)
global_explanations	Consistency check for TCAR explanations (paper Section 3.1.2)
feature_importance	Consistency check for feature importance (paper Section 3.1.3)
kernel_sensitivity	Kernel sensitivity experiment (paper Appendix A)
concept_size_impact	Impact of concept set size (paper Appendix A)
tcar_inter_concepts	Intersection between concepts (paper Appendix B)
adversarial_robustness	Robustness w.r.t. adversarial perturbation (paper Appendix G)
senn	Experiment with self-explaining neural network (paper Appendix H)

The resulting plots and data are saved here.

ECG Dataset

Our script automatically downloads the ECG dataset from Kaggle. To do so, one has to create a Kaggle token as explained here. Once the token is properly set-up, one can run our experiments with the script

python -m experiments.ecg --name experiment_name --train --plot

where the --train option should only be used one time to fit a model for all the experiments. The experiment_name parameter can take the following values:

experiment_name	description
concept_accuracy	Consistency check for CAR classifiers (paper Section 3.1.1)
statistical_significance	Statistical significance for the concepts (paper Section 3.1.1)
global_explanations	Consistency check for TCAR explanations (paper Section 3.1.2)
feature_importance	Consistency check for feature importance (paper Section 3.1.3)

The resulting plots and data are saved here.

CUB Dataset

1. Create a folder data/cub in the repo folder.

2. Download the CUB dataset, the processed CUB dataset and the Place365 dataset.

3. Extract the two downloaded archives in the created folder data/cub

4. Process the CUB dataset for the robustness experiment with the script

python -m utils.robustness

Once the dataset has been processed, one can run our experiments with the script

python -m experiments.cub --name experiment_name --train --plot

where the --train option should only be used one time to fit a model for all the experiments. The experiment_name parameter can take the following values:

experiment_name	description
concept_accuracy	Consistency check for CAR classifiers (paper Section 3.1.1)
statistical_significance	Statistical significance for the concepts (paper Section 3.1.1)
global_explanations	Consistency check for TCAR explanations (paper Section 3.1.2)
feature_importance	Consistency check for feature importance (paper Section 3.1.3)
tune_kernel	Kernel optimization experiment (paper Appendix A)
global_explanations_robustness	Robustness w.r.t. background shift (paper Appendix G)

The resulting plots and data are saved here.

SEER Dataset

1. Create a folder data/seer in the repo folder.

2. Get the raw SEER Dataset in the form of a csv file seer.csv.

3. Put the file seer.csv in the folder data/seer.

Once the dataset has been processed, one can run our experiments with the script

python -m experiments.seer --name experiment_name --train --plot

where the --train option should only be used one time to fit a model for all the experiments. The experiment_name parameter can take the following values:

experiment_name	description
concept_accuracy	Prostate cancer use case (paper Section 3.2)
global_explanations	Prostate cancer use case (paper Section 3.2)
feature_importance	Prostate cancer use case (paper Section 3.2)

The resulting plots and data are saved here.

4. Citing

If you use this code, please cite the associated paper:

@misc{https://doi.org/10.48550/arxiv.2209.11222,
  doi = {10.48550/ARXIV.2209.11222},
  url = {https://arxiv.org/abs/2209.11222},
  author = {Crabbé, Jonathan and van der Schaar, Mihaela},
  keywords = {Machine Learning (cs.LG), Artificial Intelligence (cs.AI), FOS: Computer and information sciences, FOS: Computer and information sciences},
  title = {Concept Activation Regions: A Generalized Framework For Concept-Based Explanations},
  publisher = {arXiv},
  year = {2022},
  copyright = {Creative Commons Attribution 4.0 International}
}