setLRTorch: Create setting for logistics regression model with python

In hxia/plp-git-demo: Package for patient level prediction using data in the OMOP Common Data Model

Description

Create setting for logistics regression model with python

Usage

setLRTorch(
  w_decay = c(5e-04, 0.005),
  epochs = c(20, 50, 100),
  seed = NULL,
  class_weight = 0,
  autoencoder = FALSE,
  vae = FALSE
)

Arguments

w_decay	The l2 regularisation
epochs	The number of epochs
seed	A seed for the model
class_weight	The class weight used for imbalanced data: 0: Inverse ratio between positives and negatives -1: Focal loss
autoencoder	First learn stakced autoencoder for input features, then train LR on the encoded features.
vae	First learn stakced varational autoencoder for input features, then train LR on the encoded features.

Examples

## Not run: 
model.lrTorch <- setLRTorch()

## End(Not run)

hxia/plp-git-demo documentation built on March 19, 2021, 1:54 a.m.