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accommodate nni usage pattern (#209)

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Chi Wang 2021-09-14 23:16:28 -07:00 committed by GitHub
parent a9d39b71da
commit 0ba58e0ace
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3 changed files with 57 additions and 39 deletions
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13
flaml/searcher/blendsearch.py
View File

@ -884,7 +884,7 @@ except ImportError:
pass
def extract_scalar_reward(x: Dict):
return x.get("reward")
return x.get("default")
class BlendSearchTuner(BlendSearch, NNITuner):
@ -949,12 +949,19 @@ class BlendSearchTuner(BlendSearch, NNITuner):
config[key] = qrandn(*v)
else:
raise ValueError(f"unsupported type in search_space {_type}")
add_cost_to_space(config, {}, {})
# low_cost_partial_config is passed to constructor,
# which is before update_search_space() is called
init_config = self._ls.init_config
add_cost_to_space(config, init_config, self._cat_hp_cost)
self._ls = self.LocalSearch(
{},
init_config,
self._ls.metric,
self._mode,
config,
self._ls.prune_attr,
self._ls.min_resource,
self._ls.max_resource,
self._ls.resource_multiple_factor,
cost_attr=self.cost_attr,
seed=self._ls.seed,
)

0
test/test_restore.py → test/tune/test_restore.py
View File

83
test/tune/test_searcher.py
View File

@ -1,8 +1,10 @@
from flaml.searcher.blendsearch import CFO
import numpy as np
try:
from ray import __version__ as ray_version
assert ray_version >= '1.0.0'
assert ray_version >= "1.0.0"
from ray.tune import sample
except (ImportError, AssertionError):
from flaml.tune import sample
@ -15,7 +17,7 @@ except (ImportError, AssertionError):
def test_searcher():
searcher = Searcher()
searcher = Searcher(metric=['m1', 'm2'], mode=['max', 'min'])
searcher = Searcher(metric=["m1", "m2"], mode=["max", "min"])
searcher.set_search_properties(None, None, None)
searcher.suggest = searcher.on_pause = searcher.on_unpause = lambda _: {}
searcher.on_trial_complete = lambda trial_id, result, error: None
@ -30,64 +32,73 @@ except (ImportError, AssertionError):
searcher.set_state({})
print(searcher.get_state())
import optuna
config = {
"a": optuna.distributions.UniformDistribution(6, 8),
"b": optuna.distributions.LogUniformDistribution(1e-4, 1e-2),
}
searcher = OptunaSearch(
config, points_to_evaluate=[{"a": 6, "b": 1e-3}],
evaluated_rewards=[{'m': 2}], metric='m', mode='max'
config,
points_to_evaluate=[{"a": 6, "b": 1e-3}],
evaluated_rewards=[{"m": 2}],
metric="m",
mode="max",
)
config = {
"a": sample.uniform(6, 8),
"b": sample.loguniform(1e-4, 1e-2)
}
config = {"a": sample.uniform(6, 8), "b": sample.loguniform(1e-4, 1e-2)}
searcher = OptunaSearch(
config, points_to_evaluate=[{"a": 6, "b": 1e-3}],
evaluated_rewards=[{'m': 2}], metric='m', mode='max'
config,
points_to_evaluate=[{"a": 6, "b": 1e-3}],
evaluated_rewards=[{"m": 2}],
metric="m",
mode="max",
)
searcher = OptunaSearch(
define_search_space, points_to_evaluate=[{"a": 6, "b": 1e-3}],
define_search_space,
points_to_evaluate=[{"a": 6, "b": 1e-3}],
# evaluated_rewards=[{'m': 2}], metric='m', mode='max'
mode='max'
mode="max",
)
searcher = OptunaSearch()
# searcher.set_search_properties('m', 'min', define_search_space)
searcher.set_search_properties('m', 'min', config)
searcher.suggest('t1')
searcher.on_trial_complete('t1', None, False)
searcher.suggest('t2')
searcher.on_trial_complete('t2', None, True)
searcher.suggest('t3')
searcher.on_trial_complete('t3', {'m': np.nan})
searcher.save('test/tune/optuna.pickle')
searcher.restore('test/tune/optuna.pickle')
searcher.set_search_properties("m", "min", config)
searcher.suggest("t1")
searcher.on_trial_complete("t1", None, False)
searcher.suggest("t2")
searcher.on_trial_complete("t2", None, True)
searcher.suggest("t3")
searcher.on_trial_complete("t3", {"m": np.nan})
searcher.save("test/tune/optuna.pickle")
searcher.restore("test/tune/optuna.pickle")
searcher = BlendSearch(
metric="m",
global_search_alg=searcher, metric_constraints=[("c", "<", 1)])
metric="m", global_search_alg=searcher, metric_constraints=[("c", "<", 1)]
)
searcher.set_search_properties(metric="m2", config=config)
searcher.set_search_properties(config={"time_budget_s": 0})
c = searcher.suggest('t1')
c = searcher.suggest("t1")
searcher.on_trial_complete("t1", {"config": c}, True)
c = searcher.suggest('t2')
c = searcher.suggest("t2")
searcher.on_trial_complete(
"t2", {"config": c, "m2": 1, "c": 2, "time_total_s": 1})
"t2", {"config": c, "m2": 1, "c": 2, "time_total_s": 1}
)
config1 = config.copy()
config1['_choice_'] = 0
config1["_choice_"] = 0
searcher._expand_admissible_region(
lower={'root': [{'a': 0.5}, {'a': 0.4}]},
upper={'root': [{'a': 0.9}, {'a': 0.8}]},
space={'root': config1},
lower={"root": [{"a": 0.5}, {"a": 0.4}]},
upper={"root": [{"a": 0.9}, {"a": 0.8}]},
space={"root": config1},
)
searcher = CFO(
metric='m', mode='min', space=config,
points_to_evaluate=[{'a': 7, 'b': 1e-3}, {'a': 6, 'b': 3e-4}],
evaluated_rewards=[1, 1])
metric="m",
mode="min",
space=config,
points_to_evaluate=[{"a": 7, "b": 1e-3}, {"a": 6, "b": 3e-4}],
evaluated_rewards=[1, 1],
)
searcher.suggest("t1")
searcher.suggest("t2")
searcher.on_trial_result('t3', {})
searcher.on_trial_result("t3", {})
c = searcher.generate_parameters(1)
searcher.receive_trial_result(1, c, {'reward': 0})
searcher.receive_trial_result(1, c, {"default": 0})
searcher.update_search_space(
{
"a": {
@ -99,7 +110,7 @@ except (ImportError, AssertionError):
"_type": "randint",
},
"c": {
"_value": [.1, 3],
"_value": [0.1, 3],
"_type": "uniform",
},
"d": {