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Feature interaction for GPU Hist. (#4534)

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* GPU hist Interaction Constraints.
* Duplicate related parameters.
* Add tests for CPU interaction constraint.
* Add better error reporting.
* Thorough tests.
This commit is contained in:
Jiaming Yuan
2019-06-19 18:11:02 +08:00
committed by GitHub
parent 570374effe
commit ae05948e32
14 changed files with 1201 additions and 76 deletions
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36
tests/python/test_interaction_constraints.py
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@@ -9,27 +9,36 @@ rng = np.random.RandomState(1994)
class TestInteractionConstraints(unittest.TestCase):
def test_interaction_constraints(self):
def test_interaction_constraints(self, tree_method='hist'):
x1 = np.random.normal(loc=1.0, scale=1.0, size=1000)
x2 = np.random.normal(loc=1.0, scale=1.0, size=1000)
x3 = np.random.choice([1, 2, 3], size=1000, replace=True)
y = x1 + x2 + x3 + x1 * x2 * x3 \
+ np.random.normal(loc=0.001, scale=1.0, size=1000) + 3 * np.sin(x1)
+ np.random.normal(
loc=0.001, scale=1.0, size=1000) + 3 * np.sin(x1)
X = np.column_stack((x1, x2, x3))
dtrain = xgboost.DMatrix(X, label=y)
params = {'max_depth': 3, 'eta': 0.1, 'nthread': 2, 'verbosity': 0,
'interaction_constraints': '[[0, 1]]', 'tree_method': 'hist'}
num_boost_round = 100
params = {
'max_depth': 3,
'eta': 0.1,
'nthread': 2,
'interaction_constraints': '[[0, 1]]',
'tree_method': tree_method,
'verbosity': 2
}
num_boost_round = 12
# Fit a model that only allows interaction between x1 and x2
bst = xgboost.train(params, dtrain, num_boost_round, evals=[(dtrain, 'train')])
bst = xgboost.train(
params, dtrain, num_boost_round, evals=[(dtrain, 'train')])
# Set all observations to have the same x3 values then increment
# by the same amount
def f(x):
tmat = xgboost.DMatrix(np.column_stack((x1, x2, np.repeat(x, 1000))))
tmat = xgboost.DMatrix(
np.column_stack((x1, x2, np.repeat(x, 1000))))
return bst.predict(tmat)
preds = [f(x) for x in [1, 2, 3]]
# Check incrementing x3 has the same effect on all observations
@@ -40,11 +49,16 @@ class TestInteractionConstraints(unittest.TestCase):
diff2 = preds[2] - preds[1]
assert np.all(np.abs(diff2 - diff2[0]) < 1e-4)
def test_training_accuracy(self):
def test_training_accuracy(self, tree_method='hist'):
dtrain = xgboost.DMatrix(dpath + 'agaricus.txt.train?indexing_mode=1')
dtest = xgboost.DMatrix(dpath + 'agaricus.txt.test?indexing_mode=1')
params = {'eta': 1, 'max_depth': 6, 'objective': 'binary:logistic',
'tree_method': 'hist', 'interaction_constraints': '[[1,2],[2,3,4]]'}
params = {
'eta': 1,
'max_depth': 6,
'objective': 'binary:logistic',
'tree_method': tree_method,
'interaction_constraints': '[[1,2], [2,3,4]]'
}
num_boost_round = 5
params['grow_policy'] = 'lossguide'