1f9a57d17b
Previously, we use `libsvm` as default when format is not specified. However, the dmlc data parser is not particularly robust against errors, and the most common type of error is undefined format. Along with which, we will recommend users to use other data loader instead. We will continue the maintenance of the parsers as it's currently used for many internal tests including federated learning.
109 lines
3.2 KiB
Python
109 lines
3.2 KiB
Python
import os
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import subprocess
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import tempfile
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import numpy as np
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import pytest
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import xgboost as xgb
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from xgboost import testing as tm
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pytestmark = tm.timeout(10)
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class TestOMP:
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def test_omp(self):
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dtrain, dtest = tm.load_agaricus(__file__)
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param = {'booster': 'gbtree',
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'objective': 'binary:logistic',
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'grow_policy': 'depthwise',
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'tree_method': 'hist',
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'eval_metric': 'error',
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'max_depth': 5,
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'min_child_weight': 0}
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watchlist = [(dtest, 'eval'), (dtrain, 'train')]
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num_round = 5
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def run_trial():
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res = {}
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bst = xgb.train(param, dtrain, num_round, watchlist, evals_result=res)
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metrics = [res['train']['error'][-1], res['eval']['error'][-1]]
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preds = bst.predict(dtest)
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return metrics, preds
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def consist_test(title, n):
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auc, pred = run_trial()
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for i in range(n-1):
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auc2, pred2 = run_trial()
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try:
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assert auc == auc2
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assert np.array_equal(pred, pred2)
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except Exception as e:
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print('-------test %s failed, num_trial: %d-------' % (title, i))
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raise e
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auc, pred = auc2, pred2
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return auc, pred
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print('test approx ...')
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param['tree_method'] = 'approx'
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n_trials = 10
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param['nthread'] = 1
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auc_1, pred_1 = consist_test('approx_thread_1', n_trials)
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param['nthread'] = 2
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auc_2, pred_2 = consist_test('approx_thread_2', n_trials)
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param['nthread'] = 3
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auc_3, pred_3 = consist_test('approx_thread_3', n_trials)
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assert auc_1 == auc_2 == auc_3
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assert np.array_equal(auc_1, auc_2)
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assert np.array_equal(auc_1, auc_3)
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print('test hist ...')
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param['tree_method'] = 'hist'
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param['nthread'] = 1
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auc_1, pred_1 = consist_test('hist_thread_1', n_trials)
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param['nthread'] = 2
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auc_2, pred_2 = consist_test('hist_thread_2', n_trials)
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param['nthread'] = 3
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auc_3, pred_3 = consist_test('hist_thread_3', n_trials)
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assert auc_1 == auc_2 == auc_3
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assert np.array_equal(auc_1, auc_2)
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assert np.array_equal(auc_1, auc_3)
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@pytest.mark.skipif(**tm.no_sklearn())
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def test_with_omp_thread_limit(self):
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args = [
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"python", os.path.join(
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os.path.dirname(tm.normpath(__file__)), "with_omp_limit.py"
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)
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]
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results = []
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with tempfile.TemporaryDirectory() as tmpdir:
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for i in (1, 2, 16):
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path = os.path.join(tmpdir, str(i))
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with open(path, "w") as fd:
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fd.write("\n")
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cp = args.copy()
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cp.append(path)
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env = os.environ.copy()
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env["OMP_THREAD_LIMIT"] = str(i)
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status = subprocess.call(cp, env=env)
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assert status == 0
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with open(path, "r") as fd:
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results.append(float(fd.read()))
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for auc in results:
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np.testing.assert_allclose(auc, results[0])
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