cd3d14ad0e
* new single_precision_histogram param was added. Co-authored-by: SHVETS, KIRILL <kirill.shvets@intel.com> Co-authored-by: fis <jm.yuan@outlook.com>
102 lines
4.0 KiB
Python
102 lines
4.0 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_pruner(self):
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import sklearn
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params = {'tree_method': 'exact'}
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cancer = sklearn.datasets.load_breast_cancer()
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X = cancer['data']
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y = cancer["target"]
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dtrain = xgb.DMatrix(X, y)
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booster = xgb.train(params, dtrain=dtrain, num_boost_round=10)
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grown = str(booster.get_dump())
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params = {'updater': 'prune', 'process_type': 'update', 'gamma': '0.2'}
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booster = xgb.train(params, dtrain=dtrain, num_boost_round=10,
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xgb_model=booster)
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after_prune = str(booster.get_dump())
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assert grown != after_prune
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booster = xgb.train(params, dtrain=dtrain, num_boost_round=10,
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xgb_model=booster)
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second_prune = str(booster.get_dump())
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# Second prune should not change the tree
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assert after_prune == second_prune
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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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'single_precision_histogram': [True, False]}
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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 = np.array([[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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