Fix linting on master branch (#6174)

python/ray/autoscaler/autoscaler.py

@@ -279,7 +279,7 @@ class LoadMetrics(object):
             now - t for t in self.last_heartbeat_time_by_ip.values()
         ]
         most_delayed_heartbeats = sorted(
-            list(self.last_heartbeat_time_by_ip.items()),
+            self.last_heartbeat_time_by_ip.items(),
             key=lambda pair: pair[1])[:5]
         most_delayed_heartbeats = {
             ip: (now - t)

python/ray/autoscaler/commands.py

@@ -88,17 +88,12 @@ def teardown_cluster(config_file, yes, workers_only, override_cluster_name):
             if workers_only:
                 A = []
             else:
-                A = [
-                    node_id for node_id in provider.non_terminated_nodes({
-                        TAG_RAY_NODE_TYPE: NODE_TYPE_HEAD
-                    })
-                ]
-
-            A += [
-                node_id for node_id in provider.non_terminated_nodes({
-                    TAG_RAY_NODE_TYPE: NODE_TYPE_WORKER
+                A = provider.non_terminated_nodes({
+                    TAG_RAY_NODE_TYPE: NODE_TYPE_HEAD
                 })
-            ]
+            A += provider.non_terminated_nodes({
+                TAG_RAY_NODE_TYPE: NODE_TYPE_WORKER
+            })
             return A
 
         # Loop here to check that both the head and worker nodes are actually

python/ray/experimental/signal.py

@@ -139,7 +139,7 @@ def receive(sources, timeout=None):
     # redis expects ms.
     query += str(timeout_ms)
     query += " STREAMS "
-    query += " ".join([task_id for task_id in task_id_to_sources])
+    query += " ".join(task_id_to_sources)
     query += " "
     query += " ".join([
         ray.utils.decode(signal_counters[ray.utils.hex_to_binary(task_id)])

python/ray/tests/test_actor.py

@@ -2523,7 +2523,7 @@ def test_checkpointing_on_node_failure(ray_start_cluster_2_nodes,
     """Test actor checkpointing on a remote node."""
     # Place the actor on the remote node.
     cluster = ray_start_cluster_2_nodes
-    remote_node = [node for node in cluster.worker_nodes]
+    remote_node = list(cluster.worker_nodes)
     actor_cls = ray.remote(max_reconstructions=1)(ray_checkpointable_actor_cls)
     actor = actor_cls.remote()
     while (ray.get(actor.node_id.remote()) != remote_node[0].unique_id):

python/ray/tune/tests/test_automl_searcher.py

@@ -59,7 +59,7 @@ class AutoMLSearcherTest(unittest.TestCase):
 
         self.assertEqual(len(searcher.next_trials()), 0)
         for i, trial in enumerate(trials):
-            rewards = [x for x in range(i, i + 10)]
+            rewards = list(range(i, i + 10))
             random.shuffle(rewards)
             for reward in rewards:
                 searcher.on_trial_result(trial.trial_id, {"reward": reward})

python/ray/tune/tests/test_trial_scheduler.py

@@ -590,7 +590,7 @@ class HyperbandSuite(unittest.TestCase):
     def testRemove(self):
         """Test with 4: start 1, remove 1 pending, add 2, remove 1 pending."""
         sched, runner = self.schedulerSetup(4)
-        trials = sorted(list(sched._trial_info), key=lambda t: t.trial_id)
+        trials = sorted(sched._trial_info, key=lambda t: t.trial_id)
         runner._launch_trial(trials[0])
         sched.on_trial_result(runner, trials[0], result(1, 5))
         self.assertEqual(trials[0].status, Trial.RUNNING)

python/ray/tune/web_server.py

@@ -202,7 +202,7 @@ def RunnerHandler(runner):
             path = parts.path
 
             if path == "/trials":
-                return [t for t in runner.get_trials()]
+                return list(runner.get_trials())
             else:
                 trial_id = path.split("/")[-1]
                 return runner.get_trial(trial_id)

rllib/agents/a3c/a3c_tf_policy.py

@@ -95,7 +95,7 @@ def stats(policy, train_batch):
         "policy_loss": policy.loss.pi_loss,
         "policy_entropy": policy.loss.entropy,
         "var_gnorm": tf.global_norm(
-            [x for x in policy.model.trainable_variables()]),
+            list(policy.model.trainable_variables())),
         "vf_loss": policy.loss.vf_loss,
     }
 

rllib/examples/rock_paper_scissors_multiagent.py

@@ -95,7 +95,7 @@ class AlwaysSameHeuristic(Policy):
                         info_batch=None,
                         episodes=None,
                         **kwargs):
-        return [x for x in state_batches[0]], state_batches, {}
+        return list(state_batches[0]), state_batches, {}
 
     def learn_on_batch(self, samples):
         pass

rllib/models/preprocessors.py

@@ -239,7 +239,7 @@ class DictFlatteningPreprocessor(Preprocessor):
     @override(Preprocessor)
     def write(self, observation, array, offset):
         if not isinstance(observation, OrderedDict):
-            observation = OrderedDict(sorted(list(observation.items())))
+            observation = OrderedDict(sorted(observation.items()))
         assert len(observation) == len(self.preprocessors), \
             (len(observation), len(self.preprocessors))
         for o, p in zip(observation.values(), self.preprocessors):

rllib/offline/input_reader.py

@@ -71,7 +71,7 @@ class InputReader(object):
                 "tf_input_ops() is not implemented for multi agent batches")
 
         keys = [
-            k for k in sorted(list(batch.keys()))
+            k for k in sorted(batch.keys())
             if np.issubdtype(batch[k].dtype, np.number)
         ]
         dtypes = [batch[k].dtype for k in keys]