9c9070aea2
* Do not derive from unittest.TestCase (not needed for pytest) * assertRaises -> pytest.raises * Simplify test_empty_dmatrix with test parametrization * setUpClass -> setup_class, tearDownClass -> teardown_class * Don't import unittest; import pytest * Use plain assert * Use parametrized tests in more places * Fix test_gpu_with_sklearn.py * Put back run_empty_dmatrix_reg / run_empty_dmatrix_cls * Fix test_eta_decay_gpu_hist * Add parametrized tests for monotone constraints * Fix test names * Remove test parametrization * Revise test_slice to be not flaky
63 lines
1.8 KiB
Python
63 lines
1.8 KiB
Python
import numpy as np
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import xgboost as xgb
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from numpy.testing import assert_approx_equal
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train_data = xgb.DMatrix(np.array([[1]]), label=np.array([1]))
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class TestTreeRegularization:
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def test_alpha(self):
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params = {
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'tree_method': 'exact', 'verbosity': 0,
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'objective': 'reg:squarederror',
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'eta': 1,
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'lambda': 0,
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'alpha': 0.1
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}
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model = xgb.train(params, train_data, 1)
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preds = model.predict(train_data)
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# Default prediction (with no trees) is 0.5
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# sum_grad = (0.5 - 1.0)
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# sum_hess = 1.0
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# 0.9 = 0.5 - (sum_grad - alpha * sgn(sum_grad)) / sum_hess
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assert_approx_equal(preds[0], 0.9)
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def test_lambda(self):
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params = {
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'tree_method': 'exact', 'verbosity': 0,
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'objective': 'reg:squarederror',
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'eta': 1,
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'lambda': 1,
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'alpha': 0
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}
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model = xgb.train(params, train_data, 1)
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preds = model.predict(train_data)
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# Default prediction (with no trees) is 0.5
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# sum_grad = (0.5 - 1.0)
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# sum_hess = 1.0
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# 0.75 = 0.5 - sum_grad / (sum_hess + lambda)
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assert_approx_equal(preds[0], 0.75)
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def test_alpha_and_lambda(self):
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params = {
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'tree_method': 'exact', 'verbosity': 1,
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'objective': 'reg:squarederror',
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'eta': 1,
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'lambda': 1,
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'alpha': 0.1
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}
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model = xgb.train(params, train_data, 1)
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preds = model.predict(train_data)
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# Default prediction (with no trees) is 0.5
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# sum_grad = (0.5 - 1.0)
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# sum_hess = 1.0
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# 0.7 = 0.5 - (sum_grad - alpha * sgn(sum_grad)) / (sum_hess + lambda)
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assert_approx_equal(preds[0], 0.7)
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