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Refactor linear modelling and add new coordinate descent updater (#3103)

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* Refactor linear modelling and add new coordinate descent updater

* Allow unsorted column iterator

* Add prediction cacheing to gblinear
This commit is contained in:
Rory Mitchell
2018-02-17 09:17:01 +13:00
committed by GitHub
parent 9ffe8596f2
commit 10eb05a63a
23 changed files with 1252 additions and 271 deletions
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8
src/learner.cc
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@@ -464,18 +464,18 @@ class LearnerImpl : public Learner {
// if not, initialize the column access.
inline void LazyInitDMatrix(DMatrix* p_train) {
if (tparam.tree_method == 3 || tparam.tree_method == 4 ||
tparam.tree_method == 5) {
tparam.tree_method == 5 || name_gbm_ == "gblinear") {
return;
}
monitor.Start("LazyInitDMatrix");
if (!p_train->HaveColAccess()) {
if (!p_train->HaveColAccess(true)) {
int ncol = static_cast<int>(p_train->info().num_col);
std::vector<bool> enabled(ncol, true);
// set max row per batch to limited value
// in distributed mode, use safe choice otherwise
size_t max_row_perbatch = tparam.max_row_perbatch;
const size_t safe_max_row = static_cast<size_t>(32UL << 10UL);
const size_t safe_max_row = static_cast<size_t>(32ul << 10ul);
if (tparam.tree_method == 0 && p_train->info().num_row >= (4UL << 20UL)) {
LOG(CONSOLE)
@@ -495,7 +495,7 @@ class LearnerImpl : public Learner {
max_row_perbatch = std::min(max_row_perbatch, safe_max_row);
}
// initialize column access
p_train->InitColAccess(enabled, tparam.prob_buffer_row, max_row_perbatch);
p_train->InitColAccess(enabled, tparam.prob_buffer_row, max_row_perbatch, true);
}
if (!p_train->SingleColBlock() && cfg_.count("updater") == 0) {