| Title: | STG : Feature Selection using STochastic Gates |
| Version: | 0.0.1 |
| Description: | 'STG' is a method for feature selection in neural network. The procedure is based on probabilistic relaxation of the l0 norm of features, or the count of the number of selected features. The framework simultaneously learns either a nonlinear regression or classification function while selecting a small subset of features. Read more: Yamada et al. (2020) https://proceedings.mlr.press/v119/yamada20a.html. |
| Imports: | reticulate (≥ 1.4) |
| License: | MIT + file LICENSE |
| Encoding: | UTF-8 |
| RoxygenNote: | 7.1.1 |
| NeedsCompilation: | no |
| Packaged: | 2021-12-12 02:33:36 UTC; yutaro |
| Author: | Yutaro Yamada [aut, cre] |
| Maintainer: | Yutaro Yamada <yutaro.yamada@yale.edu> |
| Repository: | CRAN |
| Date/Publication: | 2021-12-13 11:10:05 UTC |
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
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' |
|
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)
}