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more correct way to build node stats in distributed fast hist (#4140)

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* add back train method but mark as deprecated

* add back train method but mark as deprecated

* add back train method but mark as deprecated

* fix scalastyle error

* fix scalastyle error

* fix scalastyle error

* fix scalastyle error

* more changes

* temp

* update

* udpate rabit

* change the histogram

* update kfactor

* sync per node stats

* temp

* update

* final

* code clean

* update rabit

* more cleanup

* fix errors

* fix failed tests

* enforce c++11

* broadcast subsampled feature correctly

* init col

* temp

* col sampling

* fix histmastrix init

* fix col sampling

* remove cout

* fix out of bound access

* fix core dump

remove core dump file

* update

* add fid

* update

* revert some changes

* temp

* temp

* pass all tests

* bring back some tests

* recover some changes

* fix lint issue

* enable monotone and interaction constraints

* don't specify default for monotone and interactions

* recover column init part

* more recovery

* fix core dumps

* code clean

* revert some changes

* fix test compilation issue

* fix lint issue

* resolve compilation issue

* fix issues of lint caused by rebase

* fix stylistic changes and change variable names

* modularize depth width

* address the comments

* fix failed tests

* wrap perf timers with class

* temp

* pass all lossguide

* pass tests

* add comments

* more changes

* use separate flow for single and tests

* add test for lossguide hist

* remove duplications

* syncing stats for only once

* recover more changes

* recover more changes

* fix root-stats

* simplify code

* remove outdated comments
This commit is contained in:
Nan Zhu
2019-02-18 13:45:30 -08:00
committed by GitHub
parent a985a99cf0
commit 1dac5e2410
3 changed files with 40 additions and 35 deletions
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20
tests/python/test_monotone_constraints.py
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@@ -63,7 +63,7 @@ class TestMonotoneConstraints(unittest.TestCase):
# first check monotonicity for the 'exact' tree method
params_for_constrained_exact_method = {
'tree_method': 'exact', 'silent': 1,
'tree_method': 'exact', 'verbosity': 1,
'monotone_constraints': '(1, -1)'
}
constrained_exact_method = xgb.train(
@@ -71,11 +71,25 @@ class TestMonotoneConstraints(unittest.TestCase):
)
assert is_correctly_constrained(constrained_exact_method)
def test_monotone_constraints_for_hist_tree_method(self):
def test_monotone_constraints_for_depthwise_hist_tree_method(self):
# next check monotonicity for the 'hist' tree method
params_for_constrained_hist_method = {
'tree_method': 'hist', 'silent': 1,
'tree_method': 'hist', 'verbosity': 1,
'monotone_constraints': '(1, -1)'
}
constrained_hist_method = xgb.train(
params_for_constrained_hist_method, training_dset
)
assert is_correctly_constrained(constrained_hist_method)
def test_monotone_constraints_for_lossguide_hist_tree_method(self):
# next check monotonicity for the 'hist' tree method
params_for_constrained_hist_method = {
'tree_method': 'hist', 'verbosity': 1,
'grow_policy': 'lossguide',
'monotone_constraints': '(1, -1)'
}
constrained_hist_method = xgb.train(