Skip related tests when sklearn is not installed. (#4791)

tests/python/test_interaction_constraints.py

@@ -2,7 +2,8 @@
 import numpy as np
 import xgboost
 import unittest
-from sklearn.metrics import accuracy_score
+import testing as tm
+import pytest
 
 dpath = 'demo/data/'
 rng = np.random.RandomState(1994)
@@ -49,7 +50,9 @@ class TestInteractionConstraints(unittest.TestCase):
         diff2 = preds[2] - preds[1]
         assert np.all(np.abs(diff2 - diff2[0]) < 1e-4)
 
+    @pytest.mark.skipif(**tm.no_sklearn())
     def test_training_accuracy(self, tree_method='hist'):
+        from sklearn.metrics import accuracy_score
         dtrain = xgboost.DMatrix(dpath + 'agaricus.txt.train?indexing_mode=1')
         dtest = xgboost.DMatrix(dpath + 'agaricus.txt.test?indexing_mode=1')
         params = {

tests/python/test_monotone_constraints.py

@@ -1,10 +1,12 @@
 import numpy as np
 import xgboost as xgb
 import unittest
-from sklearn.metrics import accuracy_score
+import testing as tm
+import pytest
 
 dpath = 'demo/data/'
 
+
 def is_increasing(y):
     return np.count_nonzero(np.diff(y) < 0.0) == 0
 
@@ -100,7 +102,9 @@ class TestMonotoneConstraints(unittest.TestCase):
 
         assert is_correctly_constrained(constrained_hist_method)
 
+    @pytest.mark.skipif(**tm.no_sklearn())
     def test_training_accuracy(self):
+        from sklearn.metrics import accuracy_score
         dtrain = xgb.DMatrix(dpath + 'agaricus.txt.train?indexing_mode=1')
         dtest = xgb.DMatrix(dpath + 'agaricus.txt.test?indexing_mode=1')
         params = {'eta': 1, 'max_depth': 6, 'objective': 'binary:logistic',

tests/python/test_ranking.py

@@ -1,12 +1,9 @@
 import numpy as np
 from scipy.sparse import csr_matrix
 import xgboost
-import sys
 import os
-from sklearn.datasets import load_svmlight_files
 import unittest
 import itertools
-import glob
 import shutil
 import urllib.request
 import zipfile
@@ -36,6 +33,7 @@ def test_ranking_with_unweighted_data():
     auc_rec = evals_result['train']['aucpr']
     assert all(p <= q for p, q in zip(auc_rec, auc_rec[1:]))
 
+
 def test_ranking_with_weighted_data():
     Xrow = np.array([1, 2, 6, 8, 11, 14, 16, 17])
     Xcol = np.array([0, 0, 1, 1,  2,  2,  3,  3])
@@ -82,6 +80,7 @@ class TestRanking(unittest.TestCase):
         """
         Download and setup the test fixtures
         """
+        from sklearn.datasets import load_svmlight_files
         # download the test data
         cls.dpath = 'demo/rank/'
         src = 'https://s3-us-west-2.amazonaws.com/xgboost-examples/MQ2008.zip'
@@ -91,7 +90,8 @@ class TestRanking(unittest.TestCase):
         with zipfile.ZipFile(target, 'r') as f:
             f.extractall(path=cls.dpath)
 
-        x_train, y_train, qid_train, x_test, y_test, qid_test, x_valid, y_valid, qid_valid = load_svmlight_files(
+        (x_train, y_train, qid_train, x_test, y_test, qid_test,
+         x_valid, y_valid, qid_valid) = load_svmlight_files(
             (cls.dpath + "MQ2008/Fold1/train.txt",
              cls.dpath + "MQ2008/Fold1/test.txt",
              cls.dpath + "MQ2008/Fold1/vali.txt"),