Check the global explanations
All the relevant features are included.
We again generate 1000 samples with 15 features, and make 6 of them relevant for the outcome. This is a more complicated problem than the previous, as we include some non-linear effects. This time we transform the outcome into a binary variable.
i <- 1000
j <- 15
set.seed(42)
torch::torch_manual_seed(42)
X_nl <- matrix(runif(i * j, 0, 0.5), ncol = j)
y_nl <- (- 3 + 0.1 * log(abs(X_nl[, 1])) + 3 * cos(X_nl[, 2])
+ 2 * X_nl[, 3] * X_nl[, 4] + X_nl[, 5] -
X_nl[, 6] ** 2 + rnorm(i, sd = 0.1))
y <- c()
# change y to 0 and 1
y[y_nl > median(y_nl)] <- 1
y[y_nl <= median(y_nl)] <- 0
sim_data_nl <- as.data.frame(X_nl)
sim_data_nl <- cbind(sim_data_nl, y)
loaders_nl <- get_dataloaders(sim_data_nl, train_proportion = 0.9,
train_batch_size = 450, test_batch_size = 100,
standardize = FALSE)
train_loader_nl <- loaders_nl$train_loader
test_loader_nl <- loaders_nl$test_loaderWe use the same architecture as in the example with linear data. For this example, we use normalizing flows in the variational distribution.
problem <- "binary classification"
sizes <- c(j, 5, 5, 1)
incl_priors <- c(0.5, 0.5, 0.5)
stds <- c(1, 1, 1)
incl_inits <- 'polarized'
device <- "cpu"
model_nl <- lbbnn_net(problem_type = problem, sizes = sizes,
prior = incl_priors,
inclusion_inits = incl_inits, input_skip = TRUE,
std = stds, flow = TRUE, dims = c(10, 10, 10),
device = device, bias_inclusion_prob = FALSE)train_lbbnn(epochs = 20, LBBNN = model_nl,
lr = 0.2, train_dl = train_loader_nl, device = device, verbose = FALSE)
validate_lbbnn(LBBNN = model_nl, num_samples = 2, test_dl = test_loader_nl,
device = device)
#> $accuracy_full_model
#> [1] 0.65
#>
#> $accuracy_sparse
#> [1] 0.71
#>
#> $density
#> [1] 0.08205128
#>
#> $density_active_path
#> [1] 0.06153846