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search_forward <- function(p_ref, refmodel, family, intercept, nterms_max,
verbose = TRUE, opt, search_terms = NULL,
increasing_order = TRUE) {
## initialize the forward selection
## proj performs the projection over draws
projfun <- .get_proj_handle(refmodel, p_ref, family, opt$regul, intercept)
formula <- refmodel$formula
iq <- ceiling(quantile(seq_len(nterms_max), 1:10 / 10))
if (is.null(search_terms)) {
allterms <- split_formula(formula, data = refmodel$fetch_data())
} else {
allterms <- search_terms
}
chosen <- NULL
total_terms <- count_terms_in_formula(formula)
stop_search <- min(total_terms, nterms_max)
submodels <- c()
for (size in seq_len(stop_search)) {
cands <- select_possible_terms_size(chosen, allterms, size = size)
full_cands <- lapply(cands, function(cand) c(chosen, cand))
sub <- sapply(full_cands, projfun)
## select best candidate
imin <- which.min(sapply(seq_len(NCOL(sub)), function(i) {
min(unlist(sub["kl", i]))
}))
chosen <- c(chosen, cands[imin])
## append submodels
submodels <- c(submodels, sub["sub_fit", imin])
if (verbose && length(chosen) %in% iq) {
print(paste0(names(iq)[max(which(length(chosen) == iq))],
" of terms selected."))
}
}
## reduce chosen to a list of non-redundant accumulated models
return(list(solution_terms = setdiff(reduce_models(chosen), "1"),
sub_fits = submodels))
}
# copied over from search until we resolve the TODO below
search_L1_surrogate <- function(p_ref, d_train, family, intercept, nterms_max,
penalty, opt) {
## predictive mean and variance of the reference model (with parameters
## integrated out)
mu <- p_ref$mu
v <- p_ref$var
if (NCOL(mu) > 1 || NCOL(v) > 1) {
stop("Internal error: search_L1 received multiple draws. ",
"Please report to the developers.")
}
## L1-penalized projection (projection path).
## (Notice: here we use pmax = nterms_max+1 so that the computation gets
## carried until all the way down to the least regularization also for model
## size nterms_max)
search <- glm_elnet(d_train$x, mu, family,
lambda_min_ratio = opt$lambda_min_ratio, nlambda = opt$nlambda,
pmax = nterms_max + 1, pmax_strict = FALSE, offset = d_train$offset,
weights = d_train$weights, intercept = intercept, obsvar = v,
penalty = penalty, thresh = opt$thresh)
## sort the variables according to the order in which they enter the model in
## the L1-path
entering_indices <- apply(search$beta != 0, 1, function(num)
which(num)[1]) # na for those that did not enter
## variables that entered at some point
entered_variables <-
c(seq_len(NCOL(d_train$x)))[!is.na(entering_indices)]
## variables that did not enter at any point
notentered_variables <-
c(seq_len(NCOL(d_train$x)))[is.na(entering_indices)]
order_of_entered <- sort(entering_indices, index.return = TRUE)$ix
order <- c(entered_variables[order_of_entered], notentered_variables)
## fetch the coefficients corresponding to those points at the search_path
## where new variable enters
nvar <- length(order)
n <- nrow(p_ref$mu)
out <- list(
alpha = rep(NA, nterms_max + 1),
beta = matrix(0, nrow = nterms_max, ncol = nterms_max + 1),
lambda = rep(NA, nterms_max + 1),
w = matrix(NA, nrow = n, ncol = nterms_max + 1)
)
for (k in 0:nterms_max) {
if (k == 0) {
out$alpha[1] <- search$beta0[1]
out$lambda[1] <- search$lambda[1]
out$w[, 1] <- search$w[, 1]
} else {
## find those points in the L1-path where only the k most relevant
## features can have nonzero coefficient, and then fetch their
## coefficients with least regularization
ivar <- utils::tail(order, nvar - k)
steps_k_var <- which(colSums(search$beta[ivar, , drop = FALSE] != 0) == 0)
if (length(steps_k_var) > 0) {
j <- utils::tail(steps_k_var, 1)
} else {
## no steps where all the variables in set ivar would have zero
## coefficient (could be due to one or more of these variables having
## penalty = 0 so they are always in the model) so set the coefficients
## to be equal to the starting value
j <- 1
}
out$alpha[k + 1] <- search$beta0[j]
out$beta[1:k, k + 1] <- search$beta[order[1:k], j]
out$lambda[k + 1] <- search$lambda[j]
out$w[, k + 1] <- search$w[, j]
}
}
out$solution_terms <- order[1:nterms_max]
if (any(is.na(out$solution_terms)) &&
length(entered_variables) < nterms_max) {
if (length(setdiff(notentered_variables,
which(penalty == Inf))) > 0) {
warning("Less than nterms_max variables entered L1-path. ",
"Try reducing lambda_min_ratio. ")
}
}
return(out)
}
search_L1 <- function(p_ref, refmodel, family, intercept, nterms_max, penalty,
opt) {
frame <- model.frame(refmodel$formula, refmodel$fetch_data())
contrasts_arg <- get_contrasts_arg_list(
refmodel$formula,
refmodel$fetch_data()
)
x <- model.matrix(delete.intercept(refmodel$formula),
data = frame,
contrasts.arg = contrasts_arg
)
## it's important to keep the original order because that's the order
## in which lasso will estimate the parameters
tt <- terms(refmodel$formula)
terms_ <- attr(tt, "term.labels")
search_path <- search_L1_surrogate(
p_ref, list(refmodel, x = x), family,
intercept, ncol(x), penalty, opt
)
solution_terms <- collapse_contrasts_solution_path(
refmodel$formula, colnames(x)[search_path$solution_terms],
refmodel$fetch_data()
)
sub_fits <- lapply(0:length(solution_terms), function(nterms) {
if (nterms == 0) {
formula <- make_formula(c("1"))
beta <- NULL
} else {
formula <- make_formula(solution_terms[seq_len(nterms)])
variables <- unlist(lapply(
solution_terms[seq_len(nterms)],
function(term) {
form <- as.formula(paste("~ 0 +", term))
contrasts_arg <- get_contrasts_arg_list(
form,
refmodel$fetch_data()
)
return(colnames(model.matrix(form,
data = refmodel$fetch_data(),
contrasts.arg = contrasts_arg
)))
}
))
indices <- match(variables, colnames(x)[search_path$solution_terms])
beta <- search_path$beta[indices, max(indices) + 1, drop = FALSE]
}
sub <- nlist(
alpha = search_path$alpha[nterms + 1],
beta,
w = search_path$w[, nterms + 1],
formula,
x
)
class(sub) <- "subfit"
return(sub)
})
return(nlist(
solution_terms[seq_len(nterms_max)],
sub_fits[seq_len(nterms_max + 1)]
))
}
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