DAGMA algorithm to learn DAGs

Implements the DAGMA algorithm to learn DAGs from Bello et al. (2023).

Usage

dagma_fit_linear(
  X,
  loss = torch_l2_cov,
  h_func = torch_h_logdet,
  s = 1.1,
  mu = c(10, 1, 0.1, 0.01, 0),
  epoch = 5,
  l1_beta = 0.05,
  lr = 1,
  trace = FALSE
)

dagma_fit_linear_optim(
  X,
  loss = l2_cov,
  h_func = h_logdet,
  s = 1.1,
  mu = c(1, 0.1, 0.01),
  l1_beta = 1e-04,
  trace = FALSE
)

Arguments

X

A matrix of data in which each of the columns is a variable of interest in a directed acyclic graph

loss

A loss function to use. Default is L2 loss.

h_func

The continuous acyclic function to use. Default is h_logdet.

s

A regularization parameter. Should be larger than the spectral radius of X.

mu

A vector of weights to put on the loss function.

epoch

ADAM optimizer epochs.

l1_beta

L1 regularization parameter.

lr

Learning rate for ADAM optimizer.

trace

Boolean to enable progress printing

tol

Tolerance for convergence.

betas

Beta parameters for ADAM optimizer.

eps

Epsilon parameter for ADAM optimizer.

Value

A matrix of the estimated W matrix

Examples

B <- matrix(
c(0, 3, 0, 3,
 0, 0, 3, 0,
 0, 0, 0, 3,
 0, 0, 0, 0),
nrow = 4, ncol = 4, byrow = TRUE)
# Simulate from the DAG
d <- ncol(B)
X <- sim_linear_sem(B, n = 500, Sigma = 1 * diag(ncol(B)))

print(threshold_W(dagma_fit_linear(X, mu = c(10, 1, 0.1, 0.01, 0.001), l1_beta = 0.05), 0.1))
#> Error in (function (self) {    .Call(`_torch_cpp_torch_namespace_t_self_Tensor`, self)})(self = structure(c(-1.40004351672175, 0.621552721415214, -0.244199606778383, 2.06502489535922, -0.522012514745454, 0.468154420450533, 1.88850492923455, -0.826788953733443, 0.243685464746205, -1.91008746772221, 0.0700348502966383, 0.444797115792728, 0.118194874409683, -0.206087194781581, -2.27411485689294, 1.07434588225969, -1.17756330894328, 0.488628809200981, 1.33732041323558, 0.606748046557118, 1.92434334135779, -0.548257263738865, 0.433889789917249, 1.04871261975573, -0.354361163556443, -0.387213575248403, -1.75627542817088, 0.227127132728239, 0.258537287908512, 0.424858441314262, 2.03936926250284, 0.107583992213665, -0.528264082370987, 0.0817196292515542, -0.85520250080836, -0.162676337206272, 0.979956695586349, -1.50909983576966, -1.13801240059386, 0.0384995470644212, -1.90006082335695, -1.79059185585806, -0.285887914303066, 1.27067230079593, -0.473887074273069, -1.0416683808784, 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-15.7156989549809, -46.5939207139351, 9.27923581353215, -21.6203790948282, -1.88846423267305, -62.9460027762357, -47.2242317379732, 9.01779331438731, 55.3342510430459, -29.8752726794955, -18.7508807960864, 24.1410048572143, -20.4205845398133, 14.1510235538911, -26.9181085422223, -22.989611074281, 13.3013192332036, -33.6929707253413, -8.98605568822085, -33.9787332564502, 28.2460086500724, -39.5096227227347, -40.2058920475987, 43.17609895018, -21.6917072613655, -16.300374287149, -29.7096108462811, -31.9839637508967, -34.0606713911995, 34.5665620743998, 11.5283827261245, 4.77775825888831, 24.5546509159466, -14.8917701629142, -1.73073663026618, -33.8299295619377, 6.34235900764451, 4.82196407448741, 17.0118390855362, 18.5316317317269, -0.249493881293471, -11.0996823946513), dim = c(500L, 4L))): Lantern is not loaded. Please use `install_torch()` to install additional dependencies.