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[METHOD], add tree method option to prefer faster algo

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tqchen
2016-03-13 12:06:11 -07:00
parent 5fb09dc0ab
commit a2714fe052
3 changed files with 63 additions and 13 deletions
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55
src/learner.cc
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@@ -4,6 +4,7 @@
* \brief Implementation of learning algorithm.
* \author Tianqi Chen
*/
#include <xgboost/logging.h>
#include <xgboost/learner.h>
#include <dmlc/io.h>
#include <algorithm>
@@ -69,6 +70,8 @@ struct LearnerTrainParam
bool seed_per_iteration;
// data split mode, can be row, col, or none.
int dsplit;
// tree construction method
int tree_method;
// internal test flag
std::string test_flag;
// maximum buffered row value
@@ -87,6 +90,11 @@ struct LearnerTrainParam
.add_enum("col", 1)
.add_enum("row", 2)
.describe("Data split mode for distributed trainig. ");
DMLC_DECLARE_FIELD(tree_method).set_default(0)
.add_enum("auto", 0)
.add_enum("approx", 1)
.add_enum("exact", 2)
.describe("Choice of tree construction method.");
DMLC_DECLARE_FIELD(test_flag).set_default("")
.describe("Internal test flag");
DMLC_DECLARE_FIELD(prob_buffer_row).set_default(1.0f).set_range(0.0f, 1.0f)
@@ -349,21 +357,42 @@ class LearnerImpl : public Learner {
// check if p_train is ready to used by training.
// if not, initialize the column access.
inline void LazyInitDMatrix(DMatrix *p_train) {
if (p_train->HaveColAccess()) return;
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;
if (tparam.test_flag == "block" || tparam.dsplit == 2) {
max_row_perbatch = std::min(
static_cast<size_t>(32UL << 10UL), max_row_perbatch);
if (!p_train->HaveColAccess()) {
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);
if (tparam.tree_method == 0 &&
p_train->info().num_row >= (4UL << 20UL)) {
LOG(CONSOLE) << "Tree method is automatically selected to be \'approx\'"
<< " for faster speed."
<< " to use old behavior(exact greedy algorithm on single machine),"
<< " set tree_method to \'exact\'";
max_row_perbatch = std::min(max_row_perbatch, safe_max_row);
}
if (tparam.tree_method == 1) {
LOG(CONSOLE) << "Tree method is selected to be \'approx\'";
max_row_perbatch = std::min(max_row_perbatch, safe_max_row);
}
if (tparam.test_flag == "block" || tparam.dsplit == 2) {
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);
}
// initialize column access
p_train->InitColAccess(enabled,
tparam.prob_buffer_row,
max_row_perbatch);
if (!p_train->SingleColBlock() && cfg_.count("updater") == 0) {
if (tparam.tree_method == 2) {
LOG(CONSOLE) << "tree method is set to be 'exact',"
<< " but currently we are only able to proceed with approximate algorithm";
}
cfg_["updater"] = "grow_histmaker,prune";
if (gbm_.get() != nullptr) {
gbm_->Configure(cfg_.begin(), cfg_.end());