Use pytest conventions consistently (#6337)

* Do not derive from unittest.TestCase (not needed for pytest)

* assertRaises -> pytest.raises

* Simplify test_empty_dmatrix with test parametrization

* setUpClass -> setup_class, tearDownClass -> teardown_class

* Don't import unittest; import pytest

* Use plain assert

* Use parametrized tests in more places

* Fix test_gpu_with_sklearn.py

* Put back run_empty_dmatrix_reg / run_empty_dmatrix_cls

* Fix test_eta_decay_gpu_hist

* Add parametrized tests for monotone constraints

* Fix test names

* Remove test parametrization

* Revise test_slice to be not flaky

tests/python-gpu/load_pickle.py

@@ -1,6 +1,5 @@
 '''Loading a pickled model generated by test_pickling.py, only used by
 `test_gpu_with_dask.py`'''
-import unittest
 import os
 import numpy as np
 import xgboost as xgb
@@ -14,7 +13,7 @@ sys.path.append("tests/python")
 import testing as tm
 
 
-class TestLoadPickle(unittest.TestCase):
+class TestLoadPickle:
     def test_load_pkl(self):
         '''Test whether prediction is correct.'''
         assert os.environ['CUDA_VISIBLE_DEVICES'] == '-1'

tests/python-gpu/test_device_quantile_dmatrix.py

@@ -1,7 +1,6 @@
 # -*- coding: utf-8 -*-
 import numpy as np
 import xgboost as xgb
-import unittest
 import pytest
 import sys
 
@@ -9,7 +8,7 @@ sys.path.append("tests/python")
 import testing as tm
 
 
-class TestDeviceQuantileDMatrix(unittest.TestCase):
+class TestDeviceQuantileDMatrix:
     def test_dmatrix_numpy_init(self):
         data = np.random.randn(5, 5)
         with pytest.raises(TypeError,

tests/python-gpu/test_gpu_basic_models.py

@@ -1,15 +1,15 @@
 import sys
 import os
-import unittest
 import numpy as np
 import xgboost as xgb
+import pytest
 sys.path.append("tests/python")
 # Don't import the test class, otherwise they will run twice.
 import test_callback as test_cb  # noqa
 rng = np.random.RandomState(1994)
 
 
-class TestGPUBasicModels(unittest.TestCase):
+class TestGPUBasicModels:
     cputest = test_cb.TestCallbacks()
 
     def run_cls(self, X, y, deterministic):

tests/python-gpu/test_gpu_interaction_constraints.py

@@ -1,5 +1,4 @@
 import numpy as np
-import unittest
 import sys
 sys.path.append("tests/python")
 # Don't import the test class, otherwise they will run twice.
@@ -7,7 +6,7 @@ import test_interaction_constraints as test_ic  # noqa
 rng = np.random.RandomState(1994)
 
 
-class TestGPUInteractionConstraints(unittest.TestCase):
+class TestGPUInteractionConstraints:
     cputest = test_ic.TestInteractionConstraints()
 
     def test_interaction_constraints(self):

tests/python-gpu/test_gpu_pickling.py

@@ -1,6 +1,5 @@
 '''Test model IO with pickle.'''
 import pickle
-import unittest
 import numpy as np
 import subprocess
 import os
@@ -35,7 +34,7 @@ def load_pickle(path):
     return bst
 
 
-class TestPickling(unittest.TestCase):
+class TestPickling:
     args_template = [
         "pytest",
         "--verbose",

tests/python-gpu/test_gpu_prediction.py

@@ -1,6 +1,4 @@
 import sys
-import json
-import unittest
 import pytest
 
 import numpy as np
@@ -26,7 +24,7 @@ predict_parameter_strategy = strategies.fixed_dictionaries({
 })
 
 
-class TestGPUPredict(unittest.TestCase):
+class TestGPUPredict:
     def test_predict(self):
         iterations = 10
         np.random.seed(1)

tests/python-gpu/test_gpu_ranking.py

@@ -1,16 +1,15 @@
 import numpy as np
 import xgboost
 import os
-import unittest
 import itertools
 import shutil
 import urllib.request
 import zipfile
 
 
-class TestRanking(unittest.TestCase):
+class TestRanking:
     @classmethod
-    def setUpClass(cls):
+    def setup_class(cls):
         """
         Download and setup the test fixtures
         """
@@ -75,7 +74,7 @@ class TestRanking(unittest.TestCase):
                           'predictor': 'cpu_predictor'}
 
     @classmethod
-    def tearDownClass(cls):
+    def teardown_class(cls):
         """
         Cleanup test artifacts from download and unpacking
         :return:

tests/python-gpu/test_gpu_training_continuation.py

@@ -1,4 +1,3 @@
-import unittest
 import numpy as np
 import xgboost as xgb
 import json
@@ -6,7 +5,7 @@ import json
 rng = np.random.RandomState(1994)
 
 
-class TestGPUTrainingContinuation(unittest.TestCase):
+class TestGPUTrainingContinuation:
     def run_training_continuation(self, use_json):
         kRows = 64
         kCols = 32

tests/python-gpu/test_gpu_with_sklearn.py

@@ -2,7 +2,6 @@ import xgboost as xgb
 import pytest
 import sys
 import numpy as np
-import unittest
 
 sys.path.append("tests/python")
 import testing as tm               # noqa
@@ -33,8 +32,5 @@ def test_gpu_binary_classification():
             assert err < 0.1
 
 
-class TestGPUBoostFromPrediction(unittest.TestCase):
-    cpu_test = twskl.TestBoostFromPrediction()
-
-    def test_boost_from_prediction_gpu_hist(self):
-        self.cpu_test.run_boost_from_prediction('gpu_hist')
+def test_boost_from_prediction_gpu_hist():
+    cpu_test = twskl.run_boost_from_prediction('gpu_hist')

tests/python-gpu/test_monotonic_constraints.py

@@ -1,7 +1,6 @@
 import sys
 import numpy as np
 
-import unittest
 import pytest
 
 import xgboost as xgb
@@ -38,26 +37,27 @@ def assert_constraint(constraint, tree_method):
         assert non_increasing(pred)
 
 
-class TestMonotonicConstraints(unittest.TestCase):
-    @pytest.mark.skipif(**tm.no_sklearn())
-    def test_gpu_hist_basic(self):
-        assert_constraint(1, 'gpu_hist')
-        assert_constraint(-1, 'gpu_hist')
+@pytest.mark.skipif(**tm.no_sklearn())
+def test_gpu_hist_basic():
+    assert_constraint(1, 'gpu_hist')
+    assert_constraint(-1, 'gpu_hist')
 
-    def test_gpu_hist_depthwise(self):
-        params = {
-            'tree_method': 'gpu_hist',
-            'grow_policy': 'depthwise',
-            'monotone_constraints': '(1, -1)'
-        }
-        model = xgb.train(params, tmc.training_dset)
-        tmc.is_correctly_constrained(model)
 
-    def test_gpu_hist_lossguide(self):
-        params = {
-            'tree_method': 'gpu_hist',
-            'grow_policy': 'lossguide',
-            'monotone_constraints': '(1, -1)'
-        }
-        model = xgb.train(params, tmc.training_dset)
-        tmc.is_correctly_constrained(model)
+def test_gpu_hist_depthwise():
+    params = {
+        'tree_method': 'gpu_hist',
+        'grow_policy': 'depthwise',
+        'monotone_constraints': '(1, -1)'
+    }
+    model = xgb.train(params, tmc.training_dset)
+    tmc.is_correctly_constrained(model)
+
+
+def test_gpu_hist_lossguide():
+    params = {
+        'tree_method': 'gpu_hist',
+        'grow_policy': 'lossguide',
+        'monotone_constraints': '(1, -1)'
+    }
+    model = xgb.train(params, tmc.training_dset)
+    tmc.is_correctly_constrained(model)