Tune dropout rate for midae
Usage
tune_dropout_midae(
data,
dropout.grid = list(input.dropout = c(0, 0.25, 0.5), hidden.dropout = c(0, 0.25, 0.5)),
m = 5,
categorical.encoding = "embeddings",
device = "cpu",
epochs = 5,
batch.size = 32,
subsample = 1,
early.stopping.epochs = 1,
dae.params = list(),
pmm.type = NULL,
pmm.k = 5,
pmm.link = "prob",
pmm.save.vars = NULL,
loss.na.scale = FALSE,
verbose = TRUE,
print.every.n = 1,
save.model = FALSE,
path = NULL
)Arguments
- data
A dataset on which the midae model will be trained.
- dropout.grid
A list containing two vectors:
input.dropoutandhidden.dropout, each specifying the dropout rates to be tested for the input layer and hidden layers, respectively.- m
The number of imputations to perform.
- categorical.encoding
The method used for encoding categorical variables. Defaults to "embeddings".
- device
The computing device to use, either "cpu" or "cuda" for GPU.
- epochs
The number of training epochs for each model.
- batch.size
The size of the batches used in training.
- subsample
The proportion of the data to be used in training. Defaults to 1, meaning the full dataset is used.
- early.stopping.epochs
The number of epochs with no improvement after which training will be stopped.
- dae.params
A list of parameters for the denoising autoencoder.
- loss.na.scale
Boolean flag indicating whether to scale the loss function based on NA values. Defaults to FALSE.
- verbose
Boolean flag to control the verbosity of the function's output.
- print.every.n
Specifies how often (in epochs) to print the training progress. Only relevant if verbose is TRUE.
- save.model
Boolean flag indicating whether to save the trained model.
- path
File path where the model should be saved if save.model is TRUE. If NULL and save.model is TRUE, the model is saved in the current directory.
- pmm.params
A list of parameters for predictive mean matching.