def set_basic_conf(self):
from ax.service.ax_client import AxClient
space = AxSearch.convert_search_space({
"width": tune.uniform(0, 20),
"height": tune.uniform(-100, 100)
})
from ax.modelbridge.generation_strategy import (
GenerationStep,
GenerationStrategy,
)
from ax.modelbridge.registry import Models
# set generation strategy to sobol to ensure reproductibility
try:
# ax-platform>=0.2.0
gs = GenerationStrategy(steps=[
GenerationStep(
model=Models.SOBOL,
num_trials=-1,
model_kwargs={"seed": 4321},
),
])
except TypeError:
# ax-platform<0.2.0
gs = GenerationStrategy(steps=[
GenerationStep(
model=Models.SOBOL,
num_arms=-1,
model_kwargs={"seed": 4321},
),
])
client = AxClient(random_seed=4321, generation_strategy=gs)
client.create_experiment(parameters=space,
objective_name="loss",
minimize=True)
def cost(space, reporter):
reporter(loss=(space["height"] - 14)**2 - abs(space["width"] - 3))
search_alg = AxSearch(ax_client=client)
return search_alg, cost
def testAx(self):
from ray.tune.suggest.ax import AxSearch
from ax.service.ax_client import AxClient
converted_config = AxSearch.convert_search_space(self.config)
client = AxClient()
client.create_experiment(
parameters=converted_config, objective_name=self.metric_name, minimize=False
)
searcher = AxSearch(ax_client=client)
self._save(searcher)
client = AxClient()
client.create_experiment(
parameters=converted_config, objective_name=self.metric_name, minimize=False
)
searcher = AxSearch(ax_client=client)
self._restore(searcher)
def testAx(self):
from ray.tune.suggest.ax import AxSearch
from ax.service.ax_client import AxClient
converted_config = AxSearch.convert_search_space(self.config)
# At least one nan, inf, -inf and float
client = AxClient(random_seed=4321)
client.create_experiment(parameters=converted_config,
objective_name="_metric")
searcher = AxSearch(ax_client=client, metric="_metric", mode="max")
out = tune.run(_invalid_objective,
search_alg=searcher,
metric="_metric",
mode="max",
num_samples=4,
reuse_actors=False)
best_trial = out.best_trial
self.assertLessEqual(best_trial.config["report"], 2.0)
def testConvertAx(self):
from ray.tune.suggest.ax import AxSearch
from ax.service.ax_client import AxClient
config = {
"a": tune.sample.Categorical([2, 3, 4]).uniform(),
"b": {
"x": tune.sample.Integer(0, 5).quantized(2),
"y": 4,
"z": tune.sample.Float(1e-4, 1e-2).loguniform()
}
}
converted_config = AxSearch.convert_search_space(config)
ax_config = [
{
"name": "a",
"type": "choice",
"values": [2, 3, 4]
},
{
"name": "b/x",
"type": "range",
"bounds": [0, 5],
"value_type": "int"
},
{
"name": "b/y",
"type": "fixed",
"value": 4
},
{
"name": "b/z",
"type": "range",
"bounds": [1e-4, 1e-2],
"value_type": "float",
"log_scale": True
},
]
client1 = AxClient(random_seed=1234)
client1.create_experiment(parameters=converted_config)
searcher1 = AxSearch(ax_client=client1)
client2 = AxClient(random_seed=1234)
client2.create_experiment(parameters=ax_config)
searcher2 = AxSearch(ax_client=client2)
config1 = searcher1.suggest("0")
config2 = searcher2.suggest("0")
self.assertEqual(config1, config2)
self.assertIn(config1["a"], [2, 3, 4])
self.assertIn(config1["b"]["x"], list(range(5)))
self.assertEqual(config1["b"]["y"], 4)
self.assertLess(1e-4, config1["b"]["z"])
self.assertLess(config1["b"]["z"], 1e-2)
searcher = AxSearch(metric="a", mode="max")
analysis = tune.run(_mock_objective,
config=config,
search_alg=searcher,
num_samples=1)
trial = analysis.trials[0]
assert trial.config["a"] in [2, 3, 4]
mixed_config = {"a": tune.uniform(5, 6), "b": tune.uniform(8, 9)}
searcher = AxSearch(space=mixed_config, metric="a", mode="max")
config = searcher.suggest("0")
self.assertTrue(5 <= config["a"] <= 6)
self.assertTrue(8 <= config["b"] <= 9)