Fix bugs in multithreaded ApplySplitSparseData() (#2161)

* 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.

tests/python/test_openmp.py

@@ -0,0 +1,43 @@
+# -*- coding: utf-8 -*-
+from scipy.sparse import csr_matrix
+import xgboost as xgb
+import unittest
+
+
+class TestOMP(unittest.TestCase):
+    def test_omp(self):
+        # a contrived example where one node has an instance set of size 2.
+        data = [1, 1, 1, 1, 1, 1, 1, 1, 1, 1]
+        indices = [2, 1, 1, 2, 0, 0, 2, 0, 1, 3]
+        indptr = [0, 1, 2, 4, 5, 7, 9, 10]
+        A = csr_matrix((data, indices, indptr), shape=(7, 4))
+        y = [1, 1, 0, 0, 0, 1, 1]
+        dtrain = xgb.DMatrix(A, label=y)
+
+        # 1. use 3 threads to train a tree with an instance set of size 2
+        param = {'booster': 'gbtree',
+                 'objective': 'binary:logistic',
+                 'grow_policy': 'lossguide',
+                 'tree_method': 'hist',
+                 'eval_metric': 'auc',
+                 'max_depth': 0,
+                 'max_leaves': 1024,
+                 'min_child_weight': 0,
+                 'nthread': 3}
+
+        watchlist = [(dtrain, 'train')]
+        num_round = 1
+        res = {}
+        xgb.train(param, dtrain, num_round, watchlist, evals_result=res)
+        assert res['train']['auc'][-1] > 0.99
+
+        # 2. vary number of threads and test whether you get the same result
+        param['nthread'] = 1
+        res2 = {}
+        xgb.train(param, dtrain, num_round, watchlist, evals_result=res2)
+        assert res['train']['auc'][-1] == res2['train']['auc'][-1]
+
+        param['nthread'] = 2
+        res3 = {}
+        xgb.train(param, dtrain, num_round, watchlist, evals_result=res3)
+        assert res['train']['auc'][-1] == res3['train']['auc'][-1]