[backport] CI fixes (#6933)

* Relax shotgun test. (#6900)

It's non-deterministic algorithm, the test is flaky.

* Disable pylint error. (#6911)

* [CI] Skip external memory gtest on osx. (#6901)

* [CI] Fix custom metric test with empty dataset. (#6917)

* Reduce Travis environment setup time. (#6912)

* Remove unused r from travis.
* Don't update homebrew.
* Don't install indirect/unused dependencies like libgit2, wget, openssl.
* Move graphviz installation to conda.

* Relax shotgun test. (#6918)

* Relax test for decision stump in distributed environment. (#6919)

* Backport cupy fix.

tests/python/test_linear.py

@@ -57,15 +57,13 @@ class TestLinear:
         param['updater'] = 'shotgun'
         param = dataset.set_params(param)
         result = train_result(param, dataset.get_dmat(), num_rounds)['train'][dataset.metric]
-        # shotgun is non-deterministic, so we relax the test by sampling
-        # result.
+        # shotgun is non-deterministic, so we relax the test by only using first and last
+        # iteration.
         if len(result) > 2:
-            sampled_result = [score for i, score in enumerate(result)
-                              if i % 2 == 0]
-            sampled_result[-1] = result[-1]  # make sure the last one is used
+            sampled_result = (result[0], result[-1])
         else:
             sampled_result = result
-        assert tm.non_increasing(sampled_result, 1e-3)
+        assert tm.non_increasing(sampled_result)
 
     @given(parameter_strategy, strategies.integers(10, 50),
            tm.dataset_strategy, strategies.floats(1e-5, 2.0),

tests/python/test_with_dask.py

@@ -1023,7 +1023,17 @@ class TestWithDask:
                                  evals=[(m, 'train')])['history']
         note(history)
         history = history['train'][dataset.metric]
-        assert tm.non_increasing(history)
+
+        def is_stump():
+            return params["max_depth"] == 1 or params["max_leaves"] == 1
+
+        def minimum_bin():
+            return "max_bin" in params and params["max_bin"] == 2
+
+        if minimum_bin() and is_stump():
+            assert tm.non_increasing(history, tolerance=1e-3)
+        else:
+            assert tm.non_increasing(history)
         # Make sure that it's decreasing
         assert history[-1] < history[0]
 

tests/python/testing.py

@@ -272,6 +272,8 @@ def eval_error_metric(predt, dtrain: xgb.DMatrix):
     label = dtrain.get_label()
     r = np.zeros(predt.shape)
     gt = predt > 0.5
+    if predt.size == 0:
+        return "CustomErr", 0
     r[gt] = 1 - label[gt]
     le = predt <= 0.5
     r[le] = label[le]