Blame: tests/python/test_tree_regularization.py - dmlc/xgboost

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Scalable, Portable and Distributed Gradient Boosting (GBDT, GBRT or GBM) Library, for Python, R, Java, Scala, C++ and more. Runs on single machine, Hadoop, Spark, Dask, Flink and DataFlow

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Add LASSO (#3429) * Allow multiple split constraints * Replace RidgePenalty with ElasticNet * Add test for checking Ridge, LASSO, and Elastic Net are implemented 2018-07-15 16:38:26 +12:00			`import numpy as np`
			`from numpy.testing import assert_approx_equal`

Move Python testing utilities into xgboost module. (#8379) - Add typehints. - Fixes for pylint. Co-authored-by: Hyunsu Philip Cho <chohyu01@cs.washington.edu> 2022-10-26 16:56:11 +08:00			`import xgboost as xgb`

Add LASSO (#3429) * Allow multiple split constraints * Replace RidgePenalty with ElasticNet * Add test for checking Ridge, LASSO, and Elastic Net are implemented 2018-07-15 16:38:26 +12:00			`train_data = xgb.DMatrix(np.array([[1]]), label=np.array([1]))`


Use pytest conventions consistently (#6337) * 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 2020-11-19 17:00:15 -08:00			`class TestTreeRegularization:`
Add LASSO (#3429) * Allow multiple split constraints * Replace RidgePenalty with ElasticNet * Add test for checking Ridge, LASSO, and Elastic Net are implemented 2018-07-15 16:38:26 +12:00			`def test_alpha(self):`
			`params = {`
Init estimation for regression. (#8272) 2023-01-11 02:04:56 +08:00			`"tree_method": "exact",`
			`"verbosity": 0,`
			`"objective": "reg:squarederror",`
			`"eta": 1,`
			`"lambda": 0,`
			`"alpha": 0.1,`
			`"base_score": 0.5,`
Add LASSO (#3429) * Allow multiple split constraints * Replace RidgePenalty with ElasticNet * Add test for checking Ridge, LASSO, and Elastic Net are implemented 2018-07-15 16:38:26 +12:00			`}`

			`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 = {`
Init estimation for regression. (#8272) 2023-01-11 02:04:56 +08:00			`"tree_method": "exact",`
			`"verbosity": 0,`
			`"objective": "reg:squarederror",`
			`"eta": 1,`
			`"lambda": 1,`
			`"alpha": 0,`
			`"base_score": 0.5,`
Add LASSO (#3429) * Allow multiple split constraints * Replace RidgePenalty with ElasticNet * Add test for checking Ridge, LASSO, and Elastic Net are implemented 2018-07-15 16:38:26 +12:00			`}`

			`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 = {`
Init estimation for regression. (#8272) 2023-01-11 02:04:56 +08:00			`"tree_method": "exact",`
			`"verbosity": 1,`
			`"objective": "reg:squarederror",`
			`"eta": 1,`
			`"lambda": 1,`
			`"alpha": 0.1,`
			`"base_score": 0.5,`
Add LASSO (#3429) * Allow multiple split constraints * Replace RidgePenalty with ElasticNet * Add test for checking Ridge, LASSO, and Elastic Net are implemented 2018-07-15 16:38:26 +12:00			`}`

			`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)`
Restore functionality of max_depth=0 in hist (#7551) * Restore functionality of max_depth=0 in hist * Add test case 2022-01-10 09:37:44 -08:00
			`def test_unlimited_depth(self):`
			`x = np.array([[0], [1], [2], [3]])`
			`y = np.array([0, 1, 2, 3])`

			`model = xgb.XGBRegressor(`
			`n_estimators=1,`
			`eta=1,`
			`tree_method="hist",`
			`grow_policy="lossguide",`
			`reg_lambda=0,`
			`max_leaves=128,`
			`max_depth=0,`
			`).fit(x, y)`
			`assert np.array_equal(model.predict(x), y)`