2715baef64
* Bugfix 1: Fix segfault in multithreaded ApplySplitSparseData() When there are more threads than rows in rowset, some threads end up with empty ranges, causing them to crash. (iend - 1 needs to be accessible as part of algorithm) Fix: run only those threads with nonempty ranges. * Add regression test for Bugfix 1 * Moving python_omp_test to existing python test group Turns out you don't need to set "OMP_NUM_THREADS" to enable multithreading. Just add nthread parameter. * Bugfix 2: Fix corner case of ApplySplitSparseData() for categorical feature When split value is less than all cut points, split_cond is set incorrectly. Fix: set split_cond = -1 to indicate this scenario * Bugfix 3: Initialize data layout indicator before using it data_layout_ is accessed before being set; this variable determines whether feature 0 is included in feat_set. Fix: re-order code in InitData() to initialize data_layout_ first * Adding regression test for Bugfix 2 Unfortunately, no regression test for Bugfix 3, as there is no way to deterministically assign value to an uninitialized variable.
44 lines
1.6 KiB
Python
44 lines
1.6 KiB
Python
# -*- coding: utf-8 -*-
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from scipy.sparse import csr_matrix
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import xgboost as xgb
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import unittest
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class TestOMP(unittest.TestCase):
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def test_omp(self):
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# a contrived example where one node has an instance set of size 2.
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data = [1, 1, 1, 1, 1, 1, 1, 1, 1, 1]
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indices = [2, 1, 1, 2, 0, 0, 2, 0, 1, 3]
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indptr = [0, 1, 2, 4, 5, 7, 9, 10]
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A = csr_matrix((data, indices, indptr), shape=(7, 4))
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y = [1, 1, 0, 0, 0, 1, 1]
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dtrain = xgb.DMatrix(A, label=y)
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# 1. use 3 threads to train a tree with an instance set of size 2
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param = {'booster': 'gbtree',
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'objective': 'binary:logistic',
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'grow_policy': 'lossguide',
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'tree_method': 'hist',
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'eval_metric': 'auc',
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'max_depth': 0,
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'max_leaves': 1024,
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'min_child_weight': 0,
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'nthread': 3}
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watchlist = [(dtrain, 'train')]
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num_round = 1
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res = {}
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xgb.train(param, dtrain, num_round, watchlist, evals_result=res)
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assert res['train']['auc'][-1] > 0.99
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# 2. vary number of threads and test whether you get the same result
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param['nthread'] = 1
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res2 = {}
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xgb.train(param, dtrain, num_round, watchlist, evals_result=res2)
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assert res['train']['auc'][-1] == res2['train']['auc'][-1]
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param['nthread'] = 2
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res3 = {}
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xgb.train(param, dtrain, num_round, watchlist, evals_result=res3)
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assert res['train']['auc'][-1] == res3['train']['auc'][-1]
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