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Clean up syntax for supported Python versions. (#1963)

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* Use set/dict literal syntax

Ran code through [pyupgrade](https://github.com/asottile/pyupgrade). This is
supported in every Python version 2.7+.

* Drop unnecessary string format specification

No need to specify 0,1.. if paramters are passed in order.

* Revert "Drop unnecessary string format specification"

This reverts commit efa5ec85d30ff69f34e5ed93e31343fea7647bcb.

* Undo changes to cloudpickle

Drop use of set literal until cloudpickle uses it.

* Reformat code with YAPF

We need to set up a git pre-push hook to automatically run this stuff.
This commit is contained in:
Alok Singh
2018-05-03 07:45:11 -07:00
committed by Philipp Moritz
parent d85ee0bc04
commit cdf94c18a4
11 changed files with 41 additions and 37 deletions
Download Patch File Download Diff File Expand all files Collapse all files
examples/resnet/resnet_main.py
+1 -1
View File
@@ -230,7 +230,7 @@ def train():
# testing task with the current weights every 200 steps.
acc = ray.get(acc_id)
acc_id = test_actor.accuracy.remote(weight_id, step)
print("Step {0}: {1:.6f}".format(step - 200, acc))
print("Step {}: {:.6f}".format(step - 200, acc))
except KeyboardInterrupt:
pass
python/ray/common/test/test.py
+1 -1
View File
@@ -133,7 +133,7 @@ class TestObjectID(unittest.TestCase):
x = random_object_id()
y = random_object_id()
{x: y}
set([x, y])
{x, y}
class TestTask(unittest.TestCase):
python/ray/dataframe/groupby.py
+1 -1
View File
@@ -106,7 +106,7 @@ class DataFrameGroupBy(object):
@property
def groups(self):
return dict([(k, pd.Index(v)) for k, v in self._keys_and_values])
return {k: pd.Index(v) for k, v in self._keys_and_values}
def min(self, **kwargs):
return self._apply_agg_function(lambda df: df.min(**kwargs))
python/ray/experimental/tfutils.py
+1 -1
View File
@@ -49,7 +49,7 @@ class TensorFlowVariables(object):
self.sess = sess
queue = deque([loss])
variable_names = []
explored_inputs = set([loss])
explored_inputs = {loss}
# We do a BFS on the dependency graph of the input function to find
# the variables.
python/ray/plasma/test/test.py
+7 -7
View File
@@ -297,7 +297,7 @@ class TestPlasmaManager(unittest.TestCase):
self.client1.seal(obj_id1)
ready, waiting = self.client1.wait(
[obj_id1], timeout=100, num_returns=1)
self.assertEqual(set(ready), set([obj_id1]))
self.assertEqual(set(ready), {obj_id1})
self.assertEqual(waiting, [])
# Test wait if only one object available and only one object waited
@@ -307,8 +307,8 @@ class TestPlasmaManager(unittest.TestCase):
# Don't seal.
ready, waiting = self.client1.wait(
[obj_id2, obj_id1], timeout=100, num_returns=1)
self.assertEqual(set(ready), set([obj_id1]))
self.assertEqual(set(waiting), set([obj_id2]))
self.assertEqual(set(ready), {obj_id1})
self.assertEqual(set(waiting), {obj_id2})
# Test wait if object is sealed later.
obj_id3 = random_object_id()
@@ -321,14 +321,14 @@ class TestPlasmaManager(unittest.TestCase):
t.start()
ready, waiting = self.client1.wait(
[obj_id3, obj_id2, obj_id1], timeout=1000, num_returns=2)
self.assertEqual(set(ready), set([obj_id1, obj_id3]))
self.assertEqual(set(waiting), set([obj_id2]))
self.assertEqual(set(ready), {obj_id1, obj_id3})
self.assertEqual(set(waiting), {obj_id2})
# Test if the appropriate number of objects is shown if some objects
# are not ready.
ready, waiting = self.client1.wait([obj_id3, obj_id2, obj_id1], 100, 3)
self.assertEqual(set(ready), set([obj_id1, obj_id3]))
self.assertEqual(set(waiting), set([obj_id2]))
self.assertEqual(set(ready), {obj_id1, obj_id3})
self.assertEqual(set(waiting), {obj_id2})
# Don't forget to seal obj_id2.
self.client1.seal(obj_id2)
python/ray/tune/test/trial_scheduler_test.py
+8 -8
View File
@@ -688,36 +688,36 @@ class PopulationBasedTestingSuite(unittest.TestCase):
# Categorical case
assertProduces(
lambda: explore({"v": 4}, {"v": [3, 4, 8, 10]}, 0.0, lambda x: x),
set([3, 8]))
{3, 8})
assertProduces(
lambda: explore({"v": 3}, {"v": [3, 4, 8, 10]}, 0.0, lambda x: x),
set([3, 4]))
{3, 4})
assertProduces(
lambda: explore({"v": 10}, {"v": [3, 4, 8, 10]}, 0.0, lambda x: x),
set([8, 10]))
{8, 10})
assertProduces(
lambda: explore({"v": 7}, {"v": [3, 4, 8, 10]}, 0.0, lambda x: x),
set([3, 4, 8, 10]))
{3, 4, 8, 10})
assertProduces(
lambda: explore({"v": 4}, {"v": [3, 4, 8, 10]}, 1.0, lambda x: x),
set([3, 4, 8, 10]))
{3, 4, 8, 10})
# Continuous case
assertProduces(
lambda: explore(
{"v": 100}, {"v": lambda: random.choice([10, 100])}, 0.0,
lambda x: x),
set([80, 120]))
{80, 120})
assertProduces(
lambda: explore(
{"v": 100.0}, {"v": lambda: random.choice([10, 100])}, 0.0,
lambda x: x),
set([80.0, 120.0]))
{80.0, 120.0})
assertProduces(
lambda: explore(
{"v": 100.0}, {"v": lambda: random.choice([10, 100])}, 1.0,
lambda x: x),
set([10.0, 100.0]))
{10.0, 100.0})
def testYieldsTimeToOtherTrials(self):
pbt, runner = self.basicSetup()
python/ray/tune/trial_runner.py
+1 -1
View File
@@ -172,7 +172,7 @@ class TrialRunner(object):
if max_debug == start_num:
break
for local_dir in sorted(set([t.local_dir for t in self._trials])):
for local_dir in sorted({t.local_dir for t in self._trials}):
messages.append("Result logdir: {}".format(local_dir))
for state, trials in sorted(states.items()):
limit = limit_per_state[state]
python/ray/worker.py
+5 -3
View File
@@ -464,9 +464,11 @@ class Worker(object):
final_results = self.retrieve_and_deserialize(plain_object_ids, 0)
# Construct a dictionary mapping object IDs that we haven't gotten yet
# to their original index in the object_ids argument.
unready_ids = dict((plain_object_ids[i].binary(), i)
for (i, val) in enumerate(final_results)
if val is plasma.ObjectNotAvailable)
unready_ids = {
plain_object_ids[i].binary(): i
for (i, val) in enumerate(final_results)
if val is plasma.ObjectNotAvailable
}
was_blocked = (len(unready_ids) > 0)
# Try reconstructing any objects we haven't gotten yet. Try to get them
# until at least get_timeout_milliseconds milliseconds passes, then
test/actor_test.py
+9 -9
View File
@@ -774,7 +774,7 @@ class ActorsWithGPUs(unittest.TestCase):
# Make sure that no two actors are assigned to the same GPU.
locations_and_ids = ray.get(
[actor.get_location_and_ids.remote() for actor in actors])
node_names = set([location for location, gpu_id in locations_and_ids])
node_names = {location for location, gpu_id in locations_and_ids}
self.assertEqual(len(node_names), num_local_schedulers)
location_actor_combinations = []
for node_name in node_names:
@@ -815,7 +815,7 @@ class ActorsWithGPUs(unittest.TestCase):
# Make sure that no two actors are assigned to the same GPU.
locations_and_ids = ray.get(
[actor.get_location_and_ids.remote() for actor in actors1])
node_names = set([location for location, gpu_id in locations_and_ids])
node_names = {location for location, gpu_id in locations_and_ids}
self.assertEqual(len(node_names), num_local_schedulers)
# Keep track of which GPU IDs are being used for each location.
@@ -847,9 +847,9 @@ class ActorsWithGPUs(unittest.TestCase):
# Make sure that no two actors are assigned to the same GPU.
locations_and_ids = ray.get(
[actor.get_location_and_ids.remote() for actor in actors2])
self.assertEqual(
node_names,
set([location for location, gpu_id in locations_and_ids]))
self.assertEqual(node_names,
{location
for location, gpu_id in locations_and_ids})
for location, gpu_ids in locations_and_ids:
gpus_in_use[location].extend(gpu_ids)
for node_name in node_names:
@@ -887,7 +887,7 @@ class ActorsWithGPUs(unittest.TestCase):
# Make sure that no two actors are assigned to the same GPU.
locations_and_ids = ray.get(
[actor.get_location_and_ids.remote() for actor in actors])
node_names = set([location for location, gpu_id in locations_and_ids])
node_names = {location for location, gpu_id in locations_and_ids}
self.assertEqual(len(node_names), 2)
for node_name in node_names:
node_gpu_ids = [
@@ -896,8 +896,8 @@ class ActorsWithGPUs(unittest.TestCase):
]
self.assertIn(len(node_gpu_ids), [5, 10])
self.assertEqual(
set(node_gpu_ids),
set([(i, ) for i in range(len(node_gpu_ids))]))
set(node_gpu_ids), {(i, )
for i in range(len(node_gpu_ids))})
# Creating a new actor should fail because all of the GPUs are being
# used.
@@ -1942,7 +1942,7 @@ class ActorPlacementAndResources(unittest.TestCase):
results = ray.get([result1, result2, result3])
self.assertEqual(results[0], results[2])
self.assertEqual(set(results), set([0, 1]))
self.assertEqual(set(results), {0, 1})
# Make sure that when one actor goes out of scope a new actor is
# created because some resources have been freed up.
test/runtest.py
+3 -3
View File
@@ -255,7 +255,7 @@ class SerializationTest(unittest.TestCase):
# Test sets.
self.assertEqual(ray.get(f.remote(set())), set())
s = set([1, (1, 2, "hi")])
s = {1, (1, 2, "hi")}
self.assertEqual(ray.get(f.remote(s)), s)
# Test types.
@@ -1317,8 +1317,8 @@ class ResourcesTest(unittest.TestCase):
self.assertEqual(list_of_ids, 10 * [[]])
list_of_ids = ray.get([f1.remote() for _ in range(10)])
set_of_ids = set([tuple(gpu_ids) for gpu_ids in list_of_ids])
self.assertEqual(set_of_ids, set([(i, ) for i in range(10)]))
set_of_ids = {tuple(gpu_ids) for gpu_ids in list_of_ids}
self.assertEqual(set_of_ids, {(i, ) for i in range(10)})
list_of_ids = ray.get([f2.remote(), f4.remote(), f4.remote()])
all_ids = [gpu_id for gpu_ids in list_of_ids for gpu_id in gpu_ids]
test/stress_tests.py
+4 -2
View File
@@ -210,8 +210,10 @@ class ReconstructionTests(unittest.TestCase):
state._initialize_global_state(self.redis_ip_address, self.redis_port)
if os.environ.get('RAY_USE_NEW_GCS', False):
tasks = state.task_table()
local_scheduler_ids = set(
task["LocalSchedulerID"] for task in tasks.values())
local_scheduler_ids = {
task["LocalSchedulerID"]
for task in tasks.values()
}
# Make sure that all nodes in the cluster were used by checking that
# the set of local scheduler IDs that had a task scheduled or submitted