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empty search space (#295)
fix the error when an empty dictionary is passed to BlendSearch as the search space.
This commit is contained in:
Chi Wang
GitHub
parent
49f9e9f86b
commit
00da79a90b
@ -219,7 +219,7 @@ class BlendSearch(Searcher):
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else:
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self._candidate_start_points = None
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self._time_budget_s, self._num_samples = time_budget_s, num_samples
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if space:
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if space is not None:
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self._init_search()
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def set_search_properties(
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@ -5,153 +5,164 @@ try:
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assert ray_version >= "1.0.0"
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from ray.tune import sample
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use_ray = True
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except (ImportError, AssertionError):
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from flaml.tune import sample
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from flaml.searcher.suggestion import OptunaSearch, Searcher, ConcurrencyLimiter
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from flaml.searcher.blendsearch import BlendSearch, CFO, RandomSearch
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use_ray = False
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def define_search_space(trial):
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trial.suggest_float("a", 6, 8)
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trial.suggest_float("b", 1e-4, 1e-2, log=True)
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from flaml.searcher.suggestion import OptunaSearch, Searcher, ConcurrencyLimiter
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from flaml.searcher.blendsearch import BlendSearch, CFO, RandomSearch
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def test_searcher():
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searcher = Searcher()
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searcher = Searcher(metric=["m1", "m2"], mode=["max", "min"])
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searcher.set_search_properties(None, None, None)
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searcher.suggest = searcher.on_pause = searcher.on_unpause = lambda _: {}
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searcher.on_trial_complete = lambda trial_id, result, error: None
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searcher = ConcurrencyLimiter(searcher, max_concurrent=2, batch=True)
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searcher.suggest("t1")
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searcher.suggest("t2")
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searcher.on_pause("t1")
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searcher.on_unpause("t1")
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searcher.suggest("t3")
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searcher.on_trial_complete("t1", {})
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searcher.on_trial_complete("t2", {})
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searcher.set_state({})
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print(searcher.get_state())
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import optuna
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config = {
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"a": optuna.distributions.UniformDistribution(6, 8),
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"b": optuna.distributions.LogUniformDistribution(1e-4, 1e-2),
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def define_search_space(trial):
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trial.suggest_float("a", 6, 8)
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trial.suggest_float("b", 1e-4, 1e-2, log=True)
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def test_searcher():
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searcher = Searcher()
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searcher = Searcher(metric=["m1", "m2"], mode=["max", "min"])
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searcher.set_search_properties(None, None, None)
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searcher.suggest = searcher.on_pause = searcher.on_unpause = lambda _: {}
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searcher.on_trial_complete = lambda trial_id, result, error: None
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searcher = ConcurrencyLimiter(searcher, max_concurrent=2, batch=True)
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searcher.suggest("t1")
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searcher.suggest("t2")
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searcher.on_pause("t1")
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searcher.on_unpause("t1")
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searcher.suggest("t3")
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searcher.on_trial_complete("t1", {})
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searcher.on_trial_complete("t2", {})
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searcher.set_state({})
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print(searcher.get_state())
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import optuna
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config = {
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"a": optuna.distributions.UniformDistribution(6, 8),
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"b": optuna.distributions.LogUniformDistribution(1e-4, 1e-2),
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}
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searcher = OptunaSearch(
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config,
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points_to_evaluate=[{"a": 6, "b": 1e-3}],
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evaluated_rewards=[{"m": 2}],
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metric="m",
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mode="max",
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)
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config = {"a": sample.uniform(6, 8), "b": sample.loguniform(1e-4, 1e-2)}
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# searcher = OptunaSearch(
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# config,
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# points_to_evaluate=[{"a": 6, "b": 1e-3}],
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# evaluated_rewards=[{"m": 2}],
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# metric="m",
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# mode="max",
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# )
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searcher = OptunaSearch(
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define_search_space,
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points_to_evaluate=[{"a": 6, "b": 1e-3}],
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# evaluated_rewards=[{'m': 2}], metric='m', mode='max'
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mode="max",
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)
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# searcher = OptunaSearch()
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# searcher.set_search_properties('m', 'min', define_search_space)
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searcher.set_search_properties("m", "min", config)
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searcher.suggest("t1")
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searcher.on_trial_complete("t1", None, False)
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searcher.suggest("t2")
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searcher.on_trial_complete("t2", None, True)
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searcher.suggest("t3")
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searcher.on_trial_complete("t3", {"m": np.nan})
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searcher.save("test/tune/optuna.pickle")
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searcher.restore("test/tune/optuna.pickle")
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searcher = BlendSearch(
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metric="m", global_search_alg=searcher, metric_constraints=[("c", "<", 1)]
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)
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searcher.set_search_properties(
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metric="m2", config=config, setting={"time_budget_s": 0}
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)
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c = searcher.suggest("t1")
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searcher.on_trial_complete("t1", {"config": c}, True)
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c = searcher.suggest("t2")
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searcher.on_trial_complete("t2", {"config": c, "m2": 1, "c": 2, "time_total_s": 1})
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config1 = config.copy()
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config1["_choice_"] = 0
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searcher._expand_admissible_region(
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lower={"root": [{"a": 0.5}, {"a": 0.4}]},
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upper={"root": [{"a": 0.9}, {"a": 0.8}]},
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space={"root": config1},
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)
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searcher = CFO(
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metric="m",
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mode="min",
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space=config,
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points_to_evaluate=[{"a": 7, "b": 1e-3}, {"a": 6, "b": 3e-4}],
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evaluated_rewards=[1, 1],
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)
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searcher.suggest("t1")
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searcher.suggest("t2")
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searcher.on_trial_result("t3", {})
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c = searcher.generate_parameters(1)
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searcher.receive_trial_result(1, c, {"default": 0})
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searcher.update_search_space(
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{
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"a": {
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"_value": [1, 2],
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"_type": "choice",
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},
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"b": {
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"_value": [1, 3],
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"_type": "randint",
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},
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"c": {
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"_value": [0.1, 3],
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"_type": "uniform",
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},
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"d": {
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"_value": [2, 8, 2],
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"_type": "quniform",
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},
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"e": {
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"_value": [2, 8],
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"_type": "loguniform",
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},
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"f": {
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"_value": [2, 8, 2],
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"_type": "qloguniform",
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},
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"g": {
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"_value": [0, 2],
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"_type": "normal",
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},
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"h": {
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"_value": [0, 2, 2],
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"_type": "qnormal",
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},
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}
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searcher = OptunaSearch(
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config,
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points_to_evaluate=[{"a": 6, "b": 1e-3}],
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evaluated_rewards=[{"m": 2}],
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metric="m",
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mode="max",
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)
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config = {"a": sample.uniform(6, 8), "b": sample.loguniform(1e-4, 1e-2)}
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searcher = OptunaSearch(
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config,
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points_to_evaluate=[{"a": 6, "b": 1e-3}],
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evaluated_rewards=[{"m": 2}],
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metric="m",
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mode="max",
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)
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searcher = OptunaSearch(
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define_search_space,
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points_to_evaluate=[{"a": 6, "b": 1e-3}],
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# evaluated_rewards=[{'m': 2}], metric='m', mode='max'
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mode="max",
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)
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searcher = OptunaSearch()
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# searcher.set_search_properties('m', 'min', define_search_space)
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searcher.set_search_properties("m", "min", config)
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searcher.suggest("t1")
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searcher.on_trial_complete("t1", None, False)
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searcher.suggest("t2")
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searcher.on_trial_complete("t2", None, True)
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searcher.suggest("t3")
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searcher.on_trial_complete("t3", {"m": np.nan})
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searcher.save("test/tune/optuna.pickle")
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searcher.restore("test/tune/optuna.pickle")
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searcher = BlendSearch(
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metric="m", global_search_alg=searcher, metric_constraints=[("c", "<", 1)]
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)
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searcher.set_search_properties(
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metric="m2", config=config, setting={"time_budget_s": 0}
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)
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c = searcher.suggest("t1")
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searcher.on_trial_complete("t1", {"config": c}, True)
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c = searcher.suggest("t2")
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searcher.on_trial_complete(
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"t2", {"config": c, "m2": 1, "c": 2, "time_total_s": 1}
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)
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config1 = config.copy()
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config1["_choice_"] = 0
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searcher._expand_admissible_region(
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lower={"root": [{"a": 0.5}, {"a": 0.4}]},
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upper={"root": [{"a": 0.9}, {"a": 0.8}]},
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space={"root": config1},
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)
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searcher = CFO(
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metric="m",
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mode="min",
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space=config,
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points_to_evaluate=[{"a": 7, "b": 1e-3}, {"a": 6, "b": 3e-4}],
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evaluated_rewards=[1, 1],
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)
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searcher.suggest("t1")
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searcher.suggest("t2")
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searcher.on_trial_result("t3", {})
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c = searcher.generate_parameters(1)
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searcher.receive_trial_result(1, c, {"default": 0})
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searcher.update_search_space(
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{
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"a": {
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"_value": [1, 2],
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"_type": "choice",
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},
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"b": {
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"_value": [1, 3],
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"_type": "randint",
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},
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"c": {
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"_value": [0.1, 3],
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"_type": "uniform",
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},
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"d": {
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"_value": [2, 8, 2],
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"_type": "quniform",
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},
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"e": {
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"_value": [2, 8],
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"_type": "loguniform",
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},
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"f": {
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"_value": [2, 8, 2],
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"_type": "qloguniform",
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},
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"g": {
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"_value": [0, 2],
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"_type": "normal",
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},
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"h": {
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"_value": [0, 2, 2],
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"_type": "qnormal",
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},
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}
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)
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np.random.seed(7654321)
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searcher = RandomSearch(
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space=config,
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points_to_evaluate=[{"a": 7, "b": 1e-3}, {"a": 6, "b": 3e-4}],
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)
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print(searcher.suggest("t1"))
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print(searcher.suggest("t2"))
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print(searcher.suggest("t3"))
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print(searcher.suggest("t4"))
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searcher.on_trial_complete({"t1"}, {})
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searcher.on_trial_result({"t2"}, {})
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np.random.seed(654321)
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searcher = RandomSearch(
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space=config,
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points_to_evaluate=[{"a": 7, "b": 1e-3}, {"a": 6, "b": 3e-4}],
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)
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print(searcher.suggest("t1"))
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print(searcher.suggest("t2"))
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print(searcher.suggest("t3"))
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)
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np.random.seed(7654321)
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searcher = RandomSearch(
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space=config,
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points_to_evaluate=[{"a": 7, "b": 1e-3}, {"a": 6, "b": 3e-4}],
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)
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print(searcher.suggest("t1"))
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print(searcher.suggest("t2"))
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print(searcher.suggest("t3"))
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print(searcher.suggest("t4"))
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searcher.on_trial_complete({"t1"}, {})
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searcher.on_trial_result({"t2"}, {})
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np.random.seed(654321)
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searcher = RandomSearch(
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space=config,
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points_to_evaluate=[{"a": 7, "b": 1e-3}, {"a": 6, "b": 3e-4}],
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)
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print(searcher.suggest("t1"))
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print(searcher.suggest("t2"))
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print(searcher.suggest("t3"))
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searcher = RandomSearch(space={})
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print(searcher.suggest("t1"))
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searcher = BlendSearch(space={})
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print(searcher.suggest("t1"))
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from flaml import tune
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tune.run(lambda x: 1, config={}, use_ray=use_ray)
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