Simple Example

In this tutorial, we will go through a simple example involving synthetic data and a generic counterfactual generator.

Data and Classifier

Below we generate some linearly separable data and fit a simple MLP classifier with batch normalization to it. For more information on generating data and models, refer to the Handling Data and Handling Models tutorials respectively.

# Counteractual data and model:
flux_training_params.batchsize = 10
counterfactual_data = load_linearly_separable()
counterfactual_data.standardize = true
M = fit_model(counterfactual_data, :MLP, batch_norm=true)

Next, determine a target and factual class for our counterfactual search and select a random factual instance to explain.

target = 2
factual = 1
chosen = rand(findall(predict_label(M, counterfactual_data) .== factual))
x = select_factual(counterfactual_data, chosen)

Finally, we generate and visualize the generated counterfactual:

# Search:
generator = WachterGenerator()
ce = generate_counterfactual(x, target, counterfactual_data, M, generator)