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[CORE] The update process for a tree model, and its application to feature importance (#1670)

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* [CORE] allow updating trees in an existing model

* [CORE] in refresh updater, allow keeping old leaf values and update stats only

* [R-package] xgb.train mod to allow updating trees in an existing model

* [R-package] added check for nrounds when is_update

* [CORE] merge parameter declaration changes; unify their code style

* [CORE] move the update-process trees initialization to Configure; rename default process_type to 'default'; fix the trees and trees_to_update sizes comparison check

* [R-package] unit tests for the update process type

* [DOC] documentation for process_type parameter; improved docs for updater, Gamma and Tweedie; added some parameter aliases; metrics indentation and some were non-documented

* fix my sloppy merge conflict resolutions

* [CORE] add a TreeProcessType enum

* whitespace fix
This commit is contained in:
Vadim Khotilovich
2016-12-04 11:33:52 -06:00
committed by Tianqi Chen
parent 4398fbbe4a
commit a44032d095
6 changed files with 221 additions and 60 deletions
Download Patch File Download Diff File Expand all files Collapse all files

35
src/tree/param.h
View File

@@ -64,6 +64,8 @@ struct TrainParam : public dmlc::Parameter<TrainParam> {
bool cache_opt;
// whether to not print info during training.
bool silent;
// whether refresh updater needs to update the leaf values
bool refresh_leaf;
// auxiliary data structure
std::vector<int> monotone_constraints;
// declare the parameters
@@ -75,10 +77,11 @@ struct TrainParam : public dmlc::Parameter<TrainParam> {
DMLC_DECLARE_FIELD(min_split_loss)
.set_lower_bound(0.0f)
.set_default(0.0f)
.describe(
"Minimum loss reduction required to make a further partition.");
DMLC_DECLARE_FIELD(max_depth).set_lower_bound(0).set_default(6).describe(
"Maximum depth of the tree.");
.describe("Minimum loss reduction required to make a further partition.");
DMLC_DECLARE_FIELD(max_depth)
.set_lower_bound(0)
.set_default(6)
.describe("Maximum depth of the tree.");
DMLC_DECLARE_FIELD(min_child_weight)
.set_lower_bound(0.0f)
.set_default(1.0f)
@@ -100,9 +103,8 @@ struct TrainParam : public dmlc::Parameter<TrainParam> {
DMLC_DECLARE_FIELD(max_delta_step)
.set_lower_bound(0.0f)
.set_default(0.0f)
.describe(
"Maximum delta step we allow each tree's weight estimate to be. "
"If the value is set to 0, it means there is no constraint");
.describe("Maximum delta step we allow each tree's weight estimate to be. "\
"If the value is set to 0, it means there is no constraint");
DMLC_DECLARE_FIELD(subsample)
.set_range(0.0f, 1.0f)
.set_default(1.0f)
@@ -114,8 +116,7 @@ struct TrainParam : public dmlc::Parameter<TrainParam> {
DMLC_DECLARE_FIELD(colsample_bytree)
.set_range(0.0f, 1.0f)
.set_default(1.0f)
.describe(
"Subsample ratio of columns, resample on each tree construction.");
.describe("Subsample ratio of columns, resample on each tree construction.");
DMLC_DECLARE_FIELD(opt_dense_col)
.set_range(0.0f, 1.0f)
.set_default(1.0f)
@@ -127,8 +128,7 @@ struct TrainParam : public dmlc::Parameter<TrainParam> {
DMLC_DECLARE_FIELD(sketch_ratio)
.set_lower_bound(0.0f)
.set_default(2.0f)
.describe("EXP Param: Sketch accuracy related parameter of approximate "
"algorithm.");
.describe("EXP Param: Sketch accuracy related parameter of approximate algorithm.");
DMLC_DECLARE_FIELD(size_leaf_vector)
.set_lower_bound(0)
.set_default(0)
@@ -136,10 +136,15 @@ struct TrainParam : public dmlc::Parameter<TrainParam> {
DMLC_DECLARE_FIELD(parallel_option)
.set_default(0)
.describe("Different types of parallelization algorithm.");
DMLC_DECLARE_FIELD(cache_opt).set_default(true).describe(
"EXP Param: Cache aware optimization.");
DMLC_DECLARE_FIELD(silent).set_default(false).describe(
"Do not print information during training.");
DMLC_DECLARE_FIELD(cache_opt)
.set_default(true)
.describe("EXP Param: Cache aware optimization.");
DMLC_DECLARE_FIELD(silent)
.set_default(false)
.describe("Do not print information during trainig.");
DMLC_DECLARE_FIELD(refresh_leaf)
.set_default(true)
.describe("Whether the refresh updater needs to update leaf values.");
DMLC_DECLARE_FIELD(monotone_constraints)
.set_default(std::vector<int>())
.describe("Constraint of variable monotonicity");

4
src/tree/updater_refresh.cc
View File

@@ -134,7 +134,9 @@ class TreeRefresher: public TreeUpdater {
tree.stat(nid).sum_hess = static_cast<bst_float>(gstats[nid].sum_hess);
gstats[nid].SetLeafVec(param, tree.leafvec(nid));
if (tree[nid].is_leaf()) {
tree[nid].set_leaf(tree.stat(nid).base_weight * param.learning_rate);
if (param.refresh_leaf) {
tree[nid].set_leaf(tree.stat(nid).base_weight * param.learning_rate);
}
} else {
tree.stat(nid).loss_chg = static_cast<bst_float>(
gstats[tree[nid].cleft()].CalcGain(param) +