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import numpy as np
import pytest
import xgboost as xgb
from xgboost import testing as tm
dpath = 'demo/data/'
def is_increasing(y):
return np.count_nonzero(np.diff(y) < 0.0) == 0
def is_decreasing(y):
return np.count_nonzero(np.diff(y) > 0.0) == 0
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)
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,
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,
feature_names=feature_names)
monotonically_decreasing_y = learner.predict(
monotonically_decreasing_dset
)
if not (
is_increasing(monotonically_increasing_y) and
is_decreasing(monotonically_decreasing_y)
):
return False
return True
number_of_dpoints = 1000
x1_positively_correlated_with_y = np.random.random(size=number_of_dpoints)
x2_negatively_correlated_with_y = np.random.random(size=number_of_dpoints)
x = np.column_stack((
x1_positively_correlated_with_y, x2_negatively_correlated_with_y
))
zs = np.random.normal(loc=0.0, scale=0.01, size=number_of_dpoints)
y = (
5 * x1_positively_correlated_with_y +
np.sin(10 * np.pi * x1_positively_correlated_with_y) -
5 * x2_negatively_correlated_with_y -
np.cos(10 * np.pi * x2_negatively_correlated_with_y) +
zs
)
training_dset = xgb.DMatrix(x, label=y)
class TestMonotoneConstraints:
def test_monotone_constraints_for_exact_tree_method(self):
# first check monotonicity for the 'exact' tree method
params_for_constrained_exact_method = {
'tree_method': 'exact', 'verbosity': 1,
'monotone_constraints': '(1, -1)'
}
constrained_exact_method = xgb.train(
params_for_constrained_exact_method, training_dset
)
assert is_correctly_constrained(constrained_exact_method)
@pytest.mark.parametrize(
"tree_method,policy",
[
("hist", "depthwise"),
("approx", "depthwise"),
("hist", "lossguide"),
("approx", "lossguide"),
],
)
def test_monotone_constraints(self, tree_method: str, policy: str) -> None:
params_for_constrained = {
"tree_method": tree_method,
"grow_policy": policy,
"monotone_constraints": "(1, -1)",
}
constrained = xgb.train(params_for_constrained, training_dset)
assert is_correctly_constrained(constrained)
def test_monotone_constraints_tuple(self) -> None:
params_for_constrained = {"monotone_constraints": (1, -1)}
constrained = xgb.train(params_for_constrained, training_dset)
assert is_correctly_constrained(constrained)
@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',
'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
dtrain = xgb.DMatrix(dpath + "agaricus.txt.train?indexing_mode=1&format=libsvm")
dtest = xgb.DMatrix(dpath + "agaricus.txt.test?indexing_mode=1&format=libsvm")
params = {'eta': 1, 'max_depth': 6, 'objective': 'binary:logistic',
'tree_method': 'hist', 'monotone_constraints': '(1, 0)'}
num_boost_round = 5
params['grow_policy'] = 'lossguide'
bst = xgb.train(params, dtrain, num_boost_round)
pred_dtest = (bst.predict(dtest) < 0.5)
assert accuracy_score(dtest.get_label(), pred_dtest) < 0.1
params['grow_policy'] = 'depthwise'
bst = xgb.train(params, dtrain, num_boost_round)
pred_dtest = (bst.predict(dtest) < 0.5)
assert accuracy_score(dtest.get_label(), pred_dtest) < 0.1
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