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[tune] Hotfix Ax breakage when fixing backwards-compat (#8285)

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This commit is contained in:
Richard Liaw
2020-05-02 20:42:50 -07:00
committed by GitHub
parent eda526c154
commit 40dfb337bf
2 changed files with 38 additions and 29 deletions
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python/ray/tune/tests/test_tune_restore.py
+17 -11
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@@ -195,7 +195,9 @@ class HyperoptWarmStartTest(AbstractWarmStartTest, unittest.TestCase):
metric="loss",
mode="min",
random_state_seed=5,
n_initial_points=1)
n_initial_points=1,
max_concurrent=1000 # Here to avoid breaking back-compat.
)
return search_alg, cost
@@ -228,9 +230,11 @@ class SkoptWarmStartTest(AbstractWarmStartTest, unittest.TestCase):
reporter(loss=(space["height"]**2 + space["width"]**2))
search_alg = SkOptSearch(
optimizer, ["width", "height"],
optimizer,
["width", "height"],
metric="loss",
mode="min",
max_concurrent=1000, # Here to avoid breaking back-compat.
points_to_evaluate=previously_run_params,
evaluated_rewards=known_rewards)
return search_alg, cost
@@ -243,11 +247,15 @@ class NevergradWarmStartTest(AbstractWarmStartTest, unittest.TestCase):
optimizer = optimizerlib.OnePlusOne(instrumentation)
def cost(space, reporter):
reporter(
mean_loss=(space["height"] - 14)**2 - abs(space["width"] - 3))
reporter(loss=(space["height"] - 14)**2 - abs(space["width"] - 3))
search_alg = NevergradSearch(
optimizer, parameter_names, metric="mean_loss", mode="min")
optimizer,
parameter_names,
metric="loss",
mode="min",
max_concurrent=1000, # Here to avoid breaking back-compat.
)
return search_alg, cost
@@ -273,14 +281,13 @@ class SigOptWarmStartTest(AbstractWarmStartTest, unittest.TestCase):
]
def cost(space, reporter):
reporter(
mean_loss=(space["height"] - 14)**2 - abs(space["width"] - 3))
reporter(loss=(space["height"] - 14)**2 - abs(space["width"] - 3))
search_alg = SigOptSearch(
space,
name="SigOpt Example Experiment",
max_concurrent=1,
metric="mean_loss",
metric="loss",
mode="min")
return search_alg, cost
@@ -298,9 +305,8 @@ class ZOOptWarmStartTest(AbstractWarmStartTest, unittest.TestCase):
"width": (ValueType.DISCRETE, [0, 20], False)
}
def cost(dim_dict, reporter):
reporter(
loss=(dim_dict["height"] - 14)**2 - abs(dim_dict["width"] - 3))
def cost(param, reporter):
reporter(loss=(param["height"] - 14)**2 - abs(param["width"] - 3))
search_alg = ZOOptSearch(
algo="Asracos", # only support ASRacos currently