a13e29ece1
* Allow multiple split constraints * Replace RidgePenalty with ElasticNet * Add test for checking Ridge, LASSO, and Elastic Net are implemented
61 lines
1.7 KiB
Python
61 lines
1.7 KiB
Python
import numpy as np
|
|
import unittest
|
|
import xgboost as xgb
|
|
|
|
from numpy.testing import assert_approx_equal
|
|
|
|
train_data = xgb.DMatrix(np.array([[1]]), label=np.array([1]))
|
|
|
|
|
|
class TestTreeRegularization(unittest.TestCase):
|
|
def test_alpha(self):
|
|
params = {
|
|
'tree_method': 'exact', 'silent': 1, 'objective': 'reg:linear',
|
|
'eta': 1,
|
|
'lambda': 0,
|
|
'alpha': 0.1
|
|
}
|
|
|
|
model = xgb.train(params, train_data, 1)
|
|
preds = model.predict(train_data)
|
|
|
|
# Default prediction (with no trees) is 0.5
|
|
# sum_grad = (0.5 - 1.0)
|
|
# sum_hess = 1.0
|
|
# 0.9 = 0.5 - (sum_grad - alpha * sgn(sum_grad)) / sum_hess
|
|
assert_approx_equal(preds[0], 0.9)
|
|
|
|
def test_lambda(self):
|
|
params = {
|
|
'tree_method': 'exact', 'silent': 1, 'objective': 'reg:linear',
|
|
'eta': 1,
|
|
'lambda': 1,
|
|
'alpha': 0
|
|
}
|
|
|
|
model = xgb.train(params, train_data, 1)
|
|
preds = model.predict(train_data)
|
|
|
|
# Default prediction (with no trees) is 0.5
|
|
# sum_grad = (0.5 - 1.0)
|
|
# sum_hess = 1.0
|
|
# 0.75 = 0.5 - sum_grad / (sum_hess + lambda)
|
|
assert_approx_equal(preds[0], 0.75)
|
|
|
|
def test_alpha_and_lambda(self):
|
|
params = {
|
|
'tree_method': 'exact', 'silent': 1, 'objective': 'reg:linear',
|
|
'eta': 1,
|
|
'lambda': 1,
|
|
'alpha': 0.1
|
|
}
|
|
|
|
model = xgb.train(params, train_data, 1)
|
|
preds = model.predict(train_data)
|
|
|
|
# Default prediction (with no trees) is 0.5
|
|
# sum_grad = (0.5 - 1.0)
|
|
# sum_hess = 1.0
|
|
# 0.7 = 0.5 - (sum_grad - alpha * sgn(sum_grad)) / (sum_hess + lambda)
|
|
assert_approx_equal(preds[0], 0.7)
|