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Changes from Cole Trapnell #110

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merged 14 commits into from
Nov 28, 2023
Merged

Changes from Cole Trapnell #110

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0e56cd7
Pass optimization configuration options to postTreatment, so that it …
ctrapnell Feb 6, 2023
4dd7451
Various improvements for torch optimizer needed to run it on the GPU
ctrapnell Feb 6, 2023
635dd22
Compute vcov on parameters when using jackknife or bootstrap
ctrapnell Feb 10, 2023
0d5b9a7
Remove stray print statement
ctrapnell Feb 14, 2023
6a745c5
pass covariance type to PLNnetwork
maddyduran Feb 14, 2023
057fd3b
fix jackknife bug
maddyduran Feb 14, 2023
423775b
cast Sigma to dense when fetching the max penalty in order to avoid w…
ctrapnell Feb 18, 2023
f801ca6
Revert back to using fixed covariance instead of spherical as base cl…
ctrapnell Feb 18, 2023
a0a43e4
Merge branch 'dev' into torch_gpu_dev
brgew Jul 19, 2023
31b6153
changing line 85 to match PLNmodels/master
maddyduran Aug 14, 2023
ed7c181
actually putting line 85 back
maddyduran Aug 25, 2023
926c77c
Merge branch 'PLN-team:master' into torch_gpu_dev
maddyduran Aug 25, 2023
02e3501
slight code cleanup in the torch optimizer
ctrapnell Oct 17, 2023
227c6da
Merge remote-tracking branch 'upstream/master'
ctrapnell Oct 17, 2023
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2 changes: 1 addition & 1 deletion R/PLN.R
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Expand Up @@ -45,7 +45,7 @@ PLN <- function(formula, data, subset, weights, control = PLN_param()) {

## post-treatment
if (control$trace > 0) cat("\n Post-treatments...")
myPLN$postTreatment(args$Y, args$X, args$O, args$w, control$config_post)
myPLN$postTreatment(args$Y, args$X, args$O, args$w, control$config_post, control$config_optim)

if (control$trace > 0) cat("\n DONE!\n")
myPLN
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2 changes: 1 addition & 1 deletion R/PLNLDA.R
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Expand Up @@ -64,7 +64,7 @@ PLNLDA <- function(formula, data, subset, weights, grouping, control = PLN_param
myLDA$optimize(grouping, args$Y, args$X, args$O, args$w, control$config_optim)

## Post-treatment: prepare LDA visualization
myLDA$postTreatment(grouping, args$Y, args$X, args$O, control$config_post)
myLDA$postTreatment(grouping, args$Y, args$X, args$O, control$config_post, control$config_optim)

if (control$trace > 0) cat("\n DONE!\n")
myLDA
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4 changes: 2 additions & 2 deletions R/PLNLDAfit-class.R
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Expand Up @@ -85,9 +85,9 @@ PLNLDAfit <- R6Class(
## Post treatment --------------------
#' @description Update R2, fisher and std_err fields and visualization
#' @param config list controlling the post-treatment
postTreatment = function(grouping, responses, covariates, offsets, config) {
postTreatment = function(grouping, responses, covariates, offsets, config_post, config_optim) {
covariates <- cbind(covariates, model.matrix( ~ grouping + 0))
super$postTreatment(responses, covariates, offsets, config = config)
super$postTreatment(responses, covariates, offsets, config_post = config_post, config_optim = config_optim)
rownames(private$C) <- colnames(private$C) <- colnames(responses)
colnames(private$S) <- 1:self$q
if (config$trace > 1) cat("\n\tCompute LD scores for visualization...")
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2 changes: 1 addition & 1 deletion R/PLNPCA.R
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Expand Up @@ -52,7 +52,7 @@ PLNPCA <- function(formula, data, subset, weights, ranks = 1:5, control = PLNPCA
## Post-treatments: pseudo-R2, rearrange criteria and prepare PCA visualization
if (control$trace > 0) cat("\n Post-treatments")
config_post <- config_post_default_PLNPCA; config_post$trace <- control$trace
myPCA$postTreatment(config_post)
myPCA$postTreatment(config_post, control$config_optim)

if (control$trace > 0) cat("\n DONE!\n")
myPCA
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4 changes: 2 additions & 2 deletions R/PLNPCAfit-class.R
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Expand Up @@ -169,8 +169,8 @@ PLNPCAfit <- R6Class(
#' * variational_var boolean indicating whether variational Fisher information matrix should be computed to estimate the variance of the model parameters (highly underestimated). Default is FALSE.
#' * rsquared boolean indicating whether approximation of R2 based on deviance should be computed. Default is TRUE
#' * trace integer for verbosity. should be > 1 to see output in post-treatments
postTreatment = function(responses, covariates, offsets, weights, config, nullModel) {
super$postTreatment(responses, covariates, offsets, weights, config, nullModel)
postTreatment = function(responses, covariates, offsets, weights, config_post, config_optim, nullModel) {
super$postTreatment(responses, covariates, offsets, weights, config_post, config_optim, nullModel)
colnames(private$C) <- colnames(private$M) <- 1:self$q
rownames(private$C) <- colnames(responses)
self$setVisualization()
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11 changes: 6 additions & 5 deletions R/PLNfamily-class.R
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Expand Up @@ -64,17 +64,18 @@ PLNfamily <-
## %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
## Post treatment --------------------
#' @description Update fields after optimization
#' @param config a list for controlling the post-treatment.
postTreatment = function(config) {
nullModel <- nullModelPoisson(self$responses, self$covariates, self$offsets, self$weights)
#' @param config_post a list for controlling the post-treatment.
postTreatment = function(config_post, config_optim) {
#nullModel <- nullModelPoisson(self$responses, self$covariates, self$offsets, self$weights)
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No comparison to a null Poisson model in the general post-treatment of PLN families. Is it to improve speed / because it's not required for the post-treatments ?

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In our applications, the call to glm.fit inside of nullModelPoisson() can sometimes throw an exception, which ruins a perfectly good PLN fit! Since it seemed to us that nullModelPoisson() was something one only needed to do when (optionally) computing the approximate R2, we thought this call was superfluous.

for (model in self$models)
model$postTreatment(
self$responses,
self$covariates,
self$offsets,
self$weights,
config,
nullModel = nullModel
config_post=config_post,
config_optim=config_optim,
nullModel = NULL
)
},

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37 changes: 20 additions & 17 deletions R/PLNfit-class.R
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Expand Up @@ -168,7 +168,7 @@ PLNfit <- R6Class(
## PRIVATE METHODS FOR VARIANCE OF THE ESTIMATORS
## %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%

variance_variational = function(X) {
variance_variational = function(X, config = config_default_nlopt) {
## Variance of B for n data points
fisher <- Matrix::bdiag(lapply(1:self$p, function(j) {
crossprod(X, private$A[, j] * X) # t(X) %*% diag(A[, i]) %*% X
Expand Down Expand Up @@ -375,7 +375,7 @@ PLNfit <- R6Class(
#' * variational_var boolean indicating whether variational Fisher information matrix should be computed to estimate the variance of the model parameters (highly underestimated). Default is FALSE.
#' * rsquared boolean indicating whether approximation of R2 based on deviance should be computed. Default is TRUE
#' * trace integer for verbosity. should be > 1 to see output in post-treatments
postTreatment = function(responses, covariates, offsets, weights = rep(1, nrow(responses)), config, nullModel = NULL) {
postTreatment = function(responses, covariates, offsets, weights = rep(1, nrow(responses)), config_post, config_optim, nullModel = NULL) {
## PARAMATERS DIMNAMES
## Set names according to those of the data matrices. If missing, use sensible defaults
if (is.null(colnames(responses)))
Expand All @@ -392,24 +392,27 @@ PLNfit <- R6Class(

## OPTIONAL POST-TREATMENT (potentially costly)
## 1. compute and store approximated R2 with Poisson-based deviance
if (config$rsquared) {
if(config$trace > 1) cat("\n\tComputing bootstrap estimator of the variance...")
if (config_post$rsquared) {
if(config_post$trace > 1) cat("\n\tComputing approximate R^2...")
private$approx_r2(responses, covariates, offsets, weights, nullModel)
}
## 2. compute and store matrix of standard variances for B and Omega with rough variational approximation
if (config$variational_var) {
if(config$trace > 1) cat("\n\tComputing variational estimator of the variance...")
private$variance_variational(covariates)
if (config_post$variational_var) {
if(config_post$trace > 1) cat("\n\tComputing variational estimator of the variance...")
private$variance_variational(covariates, config = config_optim)
}
## 3. Jackknife estimation of bias and variance
if (config$jackknife) {
if(config$trace > 1) cat("\n\tComputing jackknife estimator of the variance...")
private$variance_jackknife(responses, covariates, offsets, weights)
if (config_post$jackknife) {
if(config_post$trace > 1) cat("\n\tComputing jackknife estimator of the variance...")
private$variance_jackknife(responses, covariates, offsets, weights, config = config_optim)
}
## 4. Bootstrap estimation of variance
if (config$bootstrap > 0) {
if(config$trace > 1) cat("\n\tComputing bootstrap estimator of the variance...")
private$variance_bootstrap(responses, covariates, offsets, weights, config$bootstrap)
if (config_post$bootstrap > 0) {
if(config_post$trace > 1) {
cat("\n\tComputing bootstrap estimator of the variance...")
print (str(config_optim))
}
private$variance_bootstrap(responses, covariates, offsets, weights, n_resamples=config_post$bootstrap, config = config_optim)
}
},

Expand Down Expand Up @@ -804,11 +807,11 @@ PLNfit_fixedcov <- R6Class(
#' * bootstrap integer indicating the number of bootstrap resamples generated to evaluate the variance of the model parameters. Default is 0 (inactivated).
#' * variational_var boolean indicating whether variational Fisher information matrix should be computed to estimate the variance of the model parameters (highly underestimated). Default is FALSE.
#' * rsquared boolean indicating whether approximation of R2 based on deviance should be computed. Default is TRUE
postTreatment = function(responses, covariates, offsets, weights = rep(1, nrow(responses)), config, nullModel = NULL) {
super$postTreatment(responses, covariates, offsets, weights, config, nullModel)
postTreatment = function(responses, covariates, offsets, weights = rep(1, nrow(responses)), config_post, config_optim, nullModel = NULL) {
super$postTreatment(responses, covariates, offsets, weights, config_post, config_optim, nullModel)
## 6. compute and store matrix of standard variances for B with sandwich correction approximation
if (config$sandwich_var) {
if(config$trace > 1) cat("\n\tComputing sandwich estimator of the variance...")
if (config_post$sandwich_var) {
if(config_post$trace > 1) cat("\n\tComputing sandwich estimator of the variance...")
private$vcov_sandwich_B(responses, covariates)
}
}
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2 changes: 1 addition & 1 deletion R/PLNmixture.R
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Expand Up @@ -60,7 +60,7 @@ PLNmixture <- function(formula, data, subset, clusters = 1:5, control = PLNmixt
## Post-treatments: Compute pseudo-R2, rearrange criteria and the visualization for PCA
if (control$trace > 0) cat("\n Post-treatments")
config_post <- config_post_default_PLNmixture; config_post$trace <- control$trace
myPLN$postTreatment(config_post)
myPLN$postTreatment(config_post, control$config_optim)

if (control$trace > 0) cat("\n DONE!\n")
myPLN
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5 changes: 3 additions & 2 deletions R/PLNmixturefit-class.R
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Expand Up @@ -281,7 +281,7 @@ PLNmixturefit <-
## Post treatment --------------------
#' @description Update fields after optimization
#' @param config a list for controlling the post-treatment
postTreatment = function(responses, covariates, offsets, weights, config, nullModel) {
postTreatment = function(responses, covariates, offsets, weights, config_post, config_optim, nullModel) {

## restoring the full design matrix (group means + covariates)
mu_k <- matrix(1, self$n, ncol = 1); colnames(mu_k) <- 'Intercept'
Expand All @@ -292,7 +292,8 @@ PLNmixturefit <-
mu_k,
offsets,
private$tau[,k_],
config,
config_post,
config_optim,
nullModel = nullModel
)
},
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14 changes: 11 additions & 3 deletions R/PLNnetwork.R
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Expand Up @@ -41,8 +41,9 @@ PLNnetwork <- function(formula, data, subset, weights, penalties = NULL, control

## Post-treatments
if (control$trace > 0) cat("\n Post-treatments")
config_post <- config_post_default_PLNnetwork; config_post$trace <- control$trace
myPLN$postTreatment(config_post)
#config_post <- config_post_default_PLNnetwork;
#config_post$trace <- control$trace
myPLN$postTreatment(control$config_post, control$config_optim)

if (control$trace > 0) cat("\n DONE!\n")
myPLN
Expand Down Expand Up @@ -85,18 +86,24 @@ PLNnetwork <- function(formula, data, subset, weights, penalties = NULL, control
#'
#' @export
PLNnetwork_param <- function(
backend = "nlopt",
backend = c("nlopt", "torch"),
trace = 1 ,
n_penalties = 30 ,
min_ratio = 0.1 ,
penalize_diagonal = TRUE ,
penalty_weights = NULL ,
config_post = list(),
config_optim = list(),
inception = NULL
) {

if (!is.null(inception)) stopifnot(isPLNfit(inception))

## post-treatment config
config_pst <- config_post_default_PLN
config_pst[names(config_post)] <- config_post
config_pst$trace <- trace

## optimization config
backend <- match.arg(backend)
stopifnot(backend %in% c("nlopt", "torch"))
Expand All @@ -123,6 +130,7 @@ PLNnetwork_param <- function(
jackknife = FALSE ,
bootstrap = 0 ,
variance = TRUE ,
config_post = config_pst ,
config_optim = config_opt ,
inception = inception ), class = "PLNmodels_param")
}
2 changes: 1 addition & 1 deletion tests/testthat/test-standard-error.R
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Expand Up @@ -95,7 +95,7 @@ test_that("Check that variance estimation are coherent in PLNfit", {
trace = 2
)

myPLN$postTreatment(Y, X, exp(log_O), config = config_post)
myPLN$postTreatment(Y, X, exp(log_O), config_post = config_post)

tr_variational <- sum(standard_error(myPLN, "variational")^2)
tr_bootstrap <- sum(standard_error(myPLN, "bootstrap")^2)
Expand Down