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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
View File

@@ -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

40
tests/python/test_monotone_constraints.py
View File

@@ -14,7 +14,7 @@ def is_decreasing(y):
return np.count_nonzero(np.diff(y) > 0.0) == 0
def is_correctly_constrained(learner):
def is_correctly_constrained(learner, feature_names=None):
n = 100
variable_x = np.linspace(0, 1, n).reshape((n, 1))
fixed_xs_values = np.linspace(0, 1, n)
@@ -22,13 +22,15 @@ def is_correctly_constrained(learner):
for i in range(n):
fixed_x = fixed_xs_values[i] * np.ones((n, 1))
monotonically_increasing_x = np.column_stack((variable_x, fixed_x))
monotonically_increasing_dset = xgb.DMatrix(monotonically_increasing_x)
monotonically_increasing_dset = xgb.DMatrix(monotonically_increasing_x,
feature_names=feature_names)
monotonically_increasing_y = learner.predict(
monotonically_increasing_dset
)
monotonically_decreasing_x = np.column_stack((fixed_x, variable_x))
monotonically_decreasing_dset = xgb.DMatrix(monotonically_decreasing_x)
monotonically_decreasing_dset = xgb.DMatrix(monotonically_decreasing_x,
feature_names=feature_names)
monotonically_decreasing_y = learner.predict(
monotonically_decreasing_dset
)
@@ -101,6 +103,38 @@ class TestMonotoneConstraints:
assert is_correctly_constrained(constrained_hist_method)
@pytest.mark.parametrize('format', [dict, list])
def test_monotone_constraints_feature_names(self, format):
# next check monotonicity when initializing monotone_constraints by feature names
params = {
'tree_method': 'hist', 'verbosity': 1,
'grow_policy': 'lossguide',
'monotone_constraints': {'feature_0': 1, 'feature_1': -1}
}
if format == list:
params = list(params.items())
with pytest.raises(ValueError):
xgb.train(params, training_dset)
feature_names =[ 'feature_0', 'feature_2']
training_dset_w_feature_names = xgb.DMatrix(x, label=y, feature_names=feature_names)
with pytest.raises(ValueError):
xgb.train(params, training_dset_w_feature_names)
feature_names =[ 'feature_0', 'feature_1']
training_dset_w_feature_names = xgb.DMatrix(x, label=y, feature_names=feature_names)
constrained_learner = xgb.train(
params, training_dset_w_feature_names
)
assert is_correctly_constrained(constrained_learner, feature_names)
@pytest.mark.skipif(**tm.no_sklearn())
def test_training_accuracy(self):
from sklearn.metrics import accuracy_score