EQRN fit function for independent data

Use the EQRN_fit_restart() wrapper instead, with data_type="iid", for better stability using fitting restart.

Usage

EQRN_fit(
  X,
  y,
  intermediate_quantiles,
  interm_lvl,
  shape_fixed = FALSE,
  net_structure = c(5, 3, 3),
  hidden_fct = torch::nnf_sigmoid,
  p_drop = 0,
  intermediate_q_feature = TRUE,
  learning_rate = 1e-04,
  L2_pen = 0,
  shape_penalty = 0,
  scale_features = TRUE,
  n_epochs = 500,
  batch_size = 256,
  X_valid = NULL,
  y_valid = NULL,
  quant_valid = NULL,
  lr_decay = 1,
  patience_decay = n_epochs,
  min_lr = 0,
  patience_stop = n_epochs,
  tol = 1e-06,
  orthogonal_gpd = TRUE,
  patience_lag = 1,
  optim_met = "adam",
  seed = NULL,
  verbose = 2,
  device = default_device()
)

Arguments

X: Matrix of covariates, for training.
y: Response variable vector to model the extreme conditional quantile of, for training.
intermediate_quantiles: Vector of intermediate conditional quantiles at level interm_lvl.
interm_lvl: Probability level for the intermediate quantiles intermediate_quantiles.
shape_fixed: Whether the shape estimate depends on the covariates or not (bool).
net_structure: Vector of integers whose length determines the number of layers in the neural network and entries the number of neurons in each corresponding successive layer. If hidden_fct=="SSNN", should instead be a named list with "scale" and "shape" vectors for the two respective sub-networks. Can also be a torch::nn_module network with correct input and output dimensions, which overrides the hidden_fct, shape_fixed and p_drop arguments.
hidden_fct: Activation function for the hidden layers. Can be either a callable function (preferably from the torch library), or one of the the strings "SNN", "SSNN" for self normalizing networks (with common or separated networks for the scale and shape estimates, respectively). In the latter cases, shape_fixed has no effect.
p_drop: Probability parameter for dropout before each hidden layer for regularization during training. alpha-dropout is used with SNNs.
intermediate_q_feature: Whether to use the intermediate_quantiles as an additional covariate, by appending it to the X matrix (bool).
learning_rate: Initial learning rate for the optimizer during training of the neural network.
L2_pen: L2 weight penalty parameter for regularization during training.
shape_penalty: Penalty parameter for the shape estimate, to potentially regularize its variation from the fixed prior estimate.
scale_features: Whether to rescale each input covariates to zero mean and unit variance before applying the network (recommended).
n_epochs: Number of training epochs.
batch_size: Batch size used during training.
X_valid: Covariates in a validation set, or NULL. Used for monitoring validation loss during training, enabling learning-rate decay and early stopping.
y_valid: Response variable in a validation set, or NULL. Used for monitoring validation loss during training, enabling learning-rate decay and early stopping.
quant_valid: Intermediate conditional quantiles at level interm_lvl in a validation set, or NULL. Used for monitoring validation loss during training, enabling learning-rate decay and early stopping.
lr_decay: Learning rate decay factor.
patience_decay: Number of epochs of non-improving validation loss before a learning-rate decay is performed.
min_lr: Minimum learning rate, under which no more decay is performed.
patience_stop: Number of epochs of non-improving validation loss before early stopping is performed.
tol: Tolerance for stopping training, in case of no significant training loss improvements.
orthogonal_gpd: Whether to use the orthogonal reparametrization of the estimated GPD parameters (recommended).
patience_lag: The validation loss is considered to be non-improving if it is larger than on any of the previous patience_lag epochs.
optim_met: DEPRECATED. Optimization algorithm to use during training. "adam" is the default.
seed: Integer random seed for reproducibility in network weight initialization.
verbose: Amount of information printed during training (0:nothing, 1:most important, 2:everything).
device: (optional) A torch::torch_device(). Defaults to default_device().

Value

An EQRN object of classes c("EQRN_iid", "EQRN"), containing the fitted network, as well as all the relevant information for its usage in other functions.