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Support configuring constraints by feature names (#6783)

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Co-authored-by: fis <jm.yuan@outlook.com>
This commit is contained in:
giladmaya
2021-04-04 01:53:33 +03:00
committed by GitHub
parent 7e06c81894
commit aa0d8f20c1
3 changed files with 124 additions and 7 deletions
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28
tests/python/test_interaction_constraints.py
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@@ -9,7 +9,7 @@ rng = np.random.RandomState(1994)
class TestInteractionConstraints:
def run_interaction_constraints(self, tree_method):
def run_interaction_constraints(self, tree_method, feature_names=None, interaction_constraints='[[0, 1]]'):
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)
@@ -17,13 +17,13 @@ class TestInteractionConstraints:
+ 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)
dtrain = xgboost.DMatrix(X, label=y, feature_names=feature_names)
params = {
'max_depth': 3,
'eta': 0.1,
'nthread': 2,
'interaction_constraints': '[[0, 1]]',
'interaction_constraints': interaction_constraints,
'tree_method': tree_method
}
num_boost_round = 12
@@ -35,7 +35,7 @@ class TestInteractionConstraints:
# by the same amount
def f(x):
tmat = xgboost.DMatrix(
np.column_stack((x1, x2, np.repeat(x, 1000))))
np.column_stack((x1, x2, np.repeat(x, 1000))), feature_names=feature_names)
return bst.predict(tmat)
preds = [f(x) for x in [1, 2, 3]]
@@ -57,6 +57,26 @@ class TestInteractionConstraints:
def test_approx_interaction_constraints(self):
self.run_interaction_constraints(tree_method='approx')
def test_interaction_constraints_feature_names(self):
with pytest.raises(ValueError):
constraints = [('feature_0', 'feature_1')]
self.run_interaction_constraints(tree_method='exact',
interaction_constraints=constraints)
with pytest.raises(ValueError):
constraints = [('feature_0', 'feature_3')]
feature_names = ['feature_0', 'feature_1', 'feature_2']
self.run_interaction_constraints(tree_method='exact',
feature_names=feature_names,
interaction_constraints=constraints)
constraints = [('feature_0', 'feature_1')]
feature_names = ['feature_0', 'feature_1', 'feature_2']
self.run_interaction_constraints(tree_method='exact',
feature_names=feature_names,
interaction_constraints=constraints)
@pytest.mark.skipif(**tm.no_sklearn())
def training_accuracy(self, tree_method):
from sklearn.metrics import accuracy_score