Component-wise boosting for DataSHIELD

The package provides functionality to conduct and visualize component-wise boosting on decentralized data. The basis is the DataSHIELD infrastructure for distributed computing. This package provides the calculation of the component-wise boosting. Note that DataSHIELD uses an option datashield.privacyLevel to indicate the minimal amount of numbers required to be allowed to share an aggregated value of these numbers. Instead of setting the option, we directly retrieve the privacy level from the DESCRIPTION file each time a function calls for it. This options is set to 5 by default.

Installation

At the moment, there is no CRAN version available. Install the development version from GitHub:

remotes::install_github("schalkdaniel/dsCWB")

Register methods

It is necessary to register the assign and aggregate methods in the OPAL administration. These methods are registered automatically when publishing the package on OPAL (see DESCRIPTION).

Note that the package needs to be installed at both locations, the server and the analysts machine.

Usage

Log into DataSHIELD

library(DSI)
library(DSOpal)

surl     = "https://opal-demo.obiba.org/"
username = "administrator"
password = "password"

builder = newDSLoginBuilder()

for (i in seq_len(3L)) {
  builder$append(
    server   = paste0("server", i),
    url      = surl,
    user     = username,
    password = password,
    table    = paste0("CNSIM.CNSIM", i)
  )
}
connections = datashield.login(logins = builder$build(), assign = TRUE)

Fit distributed component-wise boosting

library(dsCWB)

#Remove all missings:
datashield.assign(connections, "Dclean", quote(dsNaRm("D")))

symbol = "Dclean"
target = "LAB_TSC"
feature_names = c("GENDER", "DIS_DIAB", "LAB_HDL", "LAB_TRIG")

cwb = dsCWB(connections, "Dclean", target, feature_names, mstop = 100L,
  val_fraction = 0.2, patience = 3L, seed = 31415L)

# Visualize selected base learner:
plotBaselearnerTraces(cwb)

# Get log for further investigation:
l = cwb$getLog()
l$minutes = as.numeric(difftime(l$time, l$time[1], units = "mins"))

library(ggplot2)

# Plot train vs test risk:
ggplot(l, aes(x = minutes)) +
  geom_line(aes(y = risk_train, color = "Train risk")) +
  geom_line(aes(y = risk_val, color = "Val risk")) +
  labs(color = "") + xlab("Minutes") + ylab("Risk")

# Visualize effect LAB_TRIG (no site-specific corrections):
pdata_LAB_TRIG = cwb$featureEffectData("LAB_TRIG")
ggplot(pdata_LAB_TRIG, aes(x = value, y = pred)) +
  geom_line()

# Effect of GENDER (just site-specific effects):
pdata_GENDER = cwb$featureEffectData("GENDER")
ggplot(pdata_GENDER, aes(x = value, y = pred, color = server)) +
  geom_boxplot() +
  facet_grid(~ server) +
  guides(color = "none")

datashield.logout(connections)

Citing

To cite dsCWB in publications, please use:

Schalk, D., Bischl, B., & Rügamer, D. (2022). Privacy-Preserving and Lossless Distributed Estimation of High-Dimensional Generalized Additive Mixed Models. arXiv preprint arXiv:2210.07723.

@article{schalk2022dcwb,
  doi = {10.48550/ARXIV.2210.07723},
  url = {https://arxiv.org/abs/2210.07723},
  author = {Schalk, Daniel and Bischl, Bernd and Rügamer, David},
  title = {Privacy-Preserving and Lossless Distributed Estimation of High-Dimensional Generalized Additive Mixed Models},
  publisher = {arXiv},
  year = {2022},
  copyright = {Creative Commons Attribution 4.0 International}
}