Package 'Rstg'

Check whether STG Python package is available and can be loaded

Description

This is used to avoid running tests on CRAN

Usage

pystg_is_available()

Value

No return value, called for side effects

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STG: Feature Selection using Stochastic Gates

Description

STG is a method for feature selection in neural network estimation problems. The new procedure is based on probabilistic relaxation of the l0 norm of features, or the count of the number of selected features. STG simultaneously learns either a nonlinear regression or classification function while selecting a small subset of features, as described in Yamada, et al, ICML 2020.

Usage

stg(
  task_type,
  input_dim,
  output_dim,
  hidden_dims,
  activation = "relu",
  sigma = 0.5,
  lam = 0.1,
  optimizer = "Adam",
  learning_rate = 0.001,
  batch_size = 100L,
  freeze_onward = NULL,
  feature_selection = TRUE,
  weight_decay = 0.001,
  random_state = 123L,
  device = "cpu"
)

Arguments

task_type	string choose 'regression', 'classification', or 'cox'
input_dim	integer The number of features of your data (input dimension)
output_dim	integer The number of classes for 'classification'. Should be 1 for 'regression' and 'cox'
hidden_dims	vector of integers,optional,default:c(60, 20, 3) architecture vector of the neural network
activation	string the type of activation functions.
sigma	float the noise level for the gaussian distribution
lam	float the regularization parameter
optimizer	string choose 'Adam' or 'SGD'
learning_rate	float
batch_size	int
freeze_onward	integer, default:NULL the network parameters will be frozen after 'freeze_onward' epoch. This is to train the gate parameters.
feature_selection	bool
weight_decay	float
random_state	integer
device	string 'cpu' or 'cuda' (if you have GPU)

Value

a "stg" object is returned.

Examples

if (pystg_is_available()){
n_size <- 1000L;
p_size <- 20L;
stg.model <- stg(task_type='regression', input_dim=p_size, output_dim=1L,
hidden_dims = c(500,50, 10), activation='tanh',
optimizer='SGD', learning_rate=0.1, batch_size=n_size,
feature_selection=TRUE, sigma=0.5, lam=0.1, random_state=0.1)
}

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