29a1356669
* Deprecate `reg:linear' in favor of `reg:squarederror'. * Replace the use of `reg:linear'. * Replace the use of `silent`.
77 lines
3.0 KiB
Python
77 lines
3.0 KiB
Python
import testing as tm
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import unittest
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import pytest
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import xgboost as xgb
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import numpy as np
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try:
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from regression_test_utilities import run_suite, parameter_combinations, \
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assert_results_non_increasing
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except ImportError:
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None
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class TestUpdaters(unittest.TestCase):
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@pytest.mark.skipif(**tm.no_sklearn())
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def test_histmaker(self):
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variable_param = {'updater': ['grow_histmaker'], 'max_depth': [2, 8]}
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for param in parameter_combinations(variable_param):
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result = run_suite(param)
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assert_results_non_increasing(result, 1e-2)
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@pytest.mark.skipif(**tm.no_sklearn())
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def test_colmaker(self):
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variable_param = {'updater': ['grow_colmaker'], 'max_depth': [2, 8]}
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for param in parameter_combinations(variable_param):
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result = run_suite(param)
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assert_results_non_increasing(result, 1e-2)
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@pytest.mark.skipif(**tm.no_sklearn())
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def test_fast_histmaker(self):
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variable_param = {'tree_method': ['hist'],
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'max_depth': [2, 8],
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'max_bin': [2, 256],
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'grow_policy': ['depthwise', 'lossguide'],
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'max_leaves': [64, 0],
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'verbosity': [0]}
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for param in parameter_combinations(variable_param):
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result = run_suite(param)
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assert_results_non_increasing(result, 1e-2)
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# hist must be same as exact on all-categorial data
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dpath = 'demo/data/'
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ag_dtrain = xgb.DMatrix(dpath + 'agaricus.txt.train')
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ag_dtest = xgb.DMatrix(dpath + 'agaricus.txt.test')
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ag_param = {'max_depth': 2,
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'tree_method': 'hist',
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'eta': 1,
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'verbosity': 0,
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'objective': 'binary:logistic',
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'eval_metric': 'auc'}
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hist_res = {}
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exact_res = {}
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xgb.train(ag_param, ag_dtrain, 10,
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[(ag_dtrain, 'train'), (ag_dtest, 'test')],
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evals_result=hist_res)
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ag_param["tree_method"] = "exact"
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xgb.train(ag_param, ag_dtrain, 10,
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[(ag_dtrain, 'train'), (ag_dtest, 'test')],
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evals_result=exact_res)
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assert hist_res['train']['auc'] == exact_res['train']['auc']
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assert hist_res['test']['auc'] == exact_res['test']['auc']
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@pytest.mark.skipif(**tm.no_sklearn())
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def test_fast_histmaker_degenerate_case(self):
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# Test a degenerate case where the quantile sketcher won't return any
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# quantile points for a particular feature (the second feature in
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# this example). Source: https://github.com/dmlc/xgboost/issues/2943
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nan = np.nan
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param = {'missing': nan, 'tree_method': 'hist'}
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model = xgb.XGBRegressor(**param)
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X = [[6.18827160e+05, 1.73000000e+02], [6.37345679e+05, nan],
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[6.38888889e+05, nan], [6.28086420e+05, nan]]
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y = [1000000., 0., 0., 500000.]
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w = [0, 0, 1, 0]
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model.fit(X, y, sample_weight=w)
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