359023c0fa
* Speed up tests * Prevent DeviceQuantileDMatrix initialisation with numpy * Use joblib.memory * Use RandomState
107 lines
4.1 KiB
Python
107 lines
4.1 KiB
Python
import numpy as np
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import sys
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import unittest
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import pytest
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import xgboost as xgb
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sys.path.append("tests/python")
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import testing as tm
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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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def assert_gpu_results(cpu_results, gpu_results):
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for cpu_res, gpu_res in zip(cpu_results, gpu_results):
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# Check final eval result roughly equivalent
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assert np.allclose(cpu_res["eval"][-1],
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gpu_res["eval"][-1], 1e-1, 1e-1)
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datasets = ["Boston", "Cancer", "Digits", "Sparse regression",
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"Sparse regression with weights", "Small weights regression"]
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test_param = parameter_combinations({
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'gpu_id': [0],
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'max_depth': [2, 8],
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'max_leaves': [255, 4],
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'max_bin': [4, 256],
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'grow_policy': ['lossguide'],
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'single_precision_histogram': [True],
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'min_child_weight': [0],
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'lambda': [0]})
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class TestGPU(unittest.TestCase):
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def test_gpu_hist(self):
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for param in test_param:
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param['tree_method'] = 'gpu_hist'
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gpu_results = run_suite(param, select_datasets=datasets)
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assert_results_non_increasing(gpu_results, 1e-2)
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param['tree_method'] = 'hist'
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cpu_results = run_suite(param, select_datasets=datasets)
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assert_gpu_results(cpu_results, gpu_results)
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@pytest.mark.skipif(**tm.no_cupy())
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def test_gpu_hist_device_dmatrix(self):
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# DeviceDMatrix does not currently accept sparse formats
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device_dmatrix_datasets = ["Boston", "Cancer", "Digits"]
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for param in test_param:
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param['tree_method'] = 'gpu_hist'
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gpu_results_device_dmatrix = run_suite(param, select_datasets=device_dmatrix_datasets,
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DMatrixT=xgb.DeviceQuantileDMatrix,
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dmatrix_params={'max_bin': param['max_bin']})
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assert_results_non_increasing(gpu_results_device_dmatrix, 1e-2)
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gpu_results = run_suite(param, select_datasets=device_dmatrix_datasets)
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assert_gpu_results(gpu_results, gpu_results_device_dmatrix)
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# NOTE(rongou): Because the `Boston` dataset is too small, this only tests external memory mode
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# with a single page. To test multiple pages, set DMatrix::kPageSize to, say, 1024.
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def test_external_memory(self):
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for param in reversed(test_param):
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param['tree_method'] = 'gpu_hist'
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param['gpu_page_size'] = 1024
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gpu_results = run_suite(param, select_datasets=["Boston"])
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assert_results_non_increasing(gpu_results, 1e-2)
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ext_mem_results = run_suite(param, select_datasets=["Boston External Memory"])
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assert_results_non_increasing(ext_mem_results, 1e-2)
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assert_gpu_results(gpu_results, ext_mem_results)
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break
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def test_with_empty_dmatrix(self):
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# FIXME(trivialfis): This should be done with all updaters
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kRows = 0
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kCols = 100
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X = np.empty((kRows, kCols))
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y = np.empty((kRows))
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dtrain = xgb.DMatrix(X, y)
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bst = xgb.train({'verbosity': 2,
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'tree_method': 'gpu_hist',
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'gpu_id': 0},
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dtrain,
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verbose_eval=True,
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num_boost_round=6,
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evals=[(dtrain, 'Train')])
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kRows = 100
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X = np.random.randn(kRows, kCols)
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dtest = xgb.DMatrix(X)
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predictions = bst.predict(dtest)
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np.testing.assert_allclose(predictions, 0.5, 1e-6)
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@pytest.mark.mgpu
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def test_specified_gpu_id_gpu_update(self):
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variable_param = {'gpu_id': [1],
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'max_depth': [8],
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'max_leaves': [255, 4],
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'max_bin': [2, 64],
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'grow_policy': ['lossguide'],
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'tree_method': ['gpu_hist']}
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for param in parameter_combinations(variable_param):
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gpu_results = run_suite(param, select_datasets=datasets)
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assert_results_non_increasing(gpu_results, 1e-2)
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