def set_basic_conf(self):
instrumentation = 2
parameter_names = ["height", "width"]
optimizer = optimizerlib.OnePlusOne(instrumentation)
def cost(space, reporter):
reporter(
mean_loss=(space["height"] - 14)**2 - abs(space["width"] - 3))
search_alg = NevergradSearch(
optimizer, parameter_names, metric="mean_loss", mode="min")
return search_alg, cost
def test_torch_agent_function() -> None:
mgame = envs.DoubleOSeven()
game = mgame.with_agent(player_1=agents.RandomAgent(mgame)).as_single_agent()
agent = agents.TorchAgent.from_module_maker(game, agents.DenseNet)
runner = base.EnvironmentRunner(game)
agentfunction = agents.TorchAgentFunction(agent, runner)
instru = agentfunction.instrumentation
args, kwargs = instru.data_to_arguments([0] * instru.dimension)
assert not args
value = agentfunction.compute(**kwargs)
assert value in [0, 1]
# optimization
opt = optimizerlib.OnePlusOne(instru, budget=10)
opt.minimize(agentfunction.compute)
def test_torch_agent_function() -> None:
mgame = envs.DoubleOSeven()
game = mgame.with_agent(player_1=agents.RandomAgent(mgame)).as_single_agent()
agent = agents.TorchAgent.from_module_maker(game, agents.DenseNet)
runner = base.EnvironmentRunner(game)
agentfunction = agents.TorchAgentFunction(agent, runner)
instru = agentfunction.parametrization
args, kwargs = instru.spawn_child().set_standardized_data([0] * instru.dimension).value
assert not args
value = agentfunction.compute(**kwargs)
assert value in [0, -1] # negated reward, for minimization
# optimization
opt = optimizerlib.OnePlusOne(instru, budget=10)
opt.minimize(agentfunction.compute)
def set_basic_conf(self):
instrumentation = 2
parameter_names = ["height", "width"]
optimizer = optimizerlib.OnePlusOne(instrumentation)
def cost(space, reporter):
reporter(loss=(space["height"] - 14)**2 - abs(space["width"] - 3))
search_alg = NevergradSearch(
optimizer,
parameter_names,
metric="loss",
mode="min",
max_concurrent=1000, # Here to avoid breaking back-compat.
)
return search_alg, cost
if __name__ == "__main__":
import argparse
from nevergrad.optimization import optimizerlib
parser = argparse.ArgumentParser()
parser.add_argument("--smoke-test",
action="store_true",
help="Finish quickly for testing")
args, _ = parser.parse_known_args()
ray.init()
config = {
"num_samples": 10 if args.smoke_test else 50,
"config": {
"iterations": 100,
},
"stop": {
"timesteps_total": 100
}
}
optimizer = optimizerlib.OnePlusOne(dimension=2)
algo = NevergradSearch(optimizer, ["height", "width"],
max_concurrent=4,
reward_attr="neg_mean_loss")
scheduler = AsyncHyperBandScheduler(reward_attr="neg_mean_loss")
run(easy_objective,
name="nevergrad",
search_alg=algo,
scheduler=scheduler,
**config)
from nevergrad.optimization import optimizerlib
def square(x):
return (x - .5)**2
optimizer = optimizerlib.OnePlusOne(dimension=1, budget=100)
# alternatively, you can use optimizerlib.registry which is a dict containing all optimizer classes
recommendation = optimizer.optimize(square, executor=None, batch_mode=True)
config = {
"num_samples": 10 if args.smoke_test else 50,
"config": {
"iterations": 100,
},
"stop": {
"timesteps_total": 100
}
}
instrumentation = 2
parameter_names = ["height", "width"]
# With nevergrad v0.2.0+ the following is also possible:
# from nevergrad import instrumentation as inst
# instrumentation = inst.Instrumentation(
# height=inst.var.Array(1).bounded(0, 200).asfloat(),
# width=inst.var.OrderedDiscrete([0, 10, 20, 30, 40, 50]))
# parameter_names = None # names are provided by the instrumentation
optimizer = optimizerlib.OnePlusOne(instrumentation)
algo = NevergradSearch(optimizer,
parameter_names,
max_concurrent=4,
metric="mean_loss",
mode="min")
scheduler = AsyncHyperBandScheduler(metric="mean_loss", mode="min")
run(easy_objective,
name="nevergrad",
search_alg=algo,
scheduler=scheduler,
**config)
from nevergrad.instrumentation import FolderFunction
from nevergrad.optimization import optimizerlib
from concurrent import futures
folder = "/project/snoplus/machine_learning/hyperparameter"
command = ["python", "hyperparameter/ng_script.py"
] # command to run from right outside the provided folder
func = FolderFunction(folder, command, clean_copy=True)
print(func.dimension) # will print the number of variables of the function
optimizer = optimizerlib.OnePlusOne(dimension=func.dimension,
budget=50,
num_workers=1)
recommendation = optimizer.optimize(func, executor=None, batch_mode=True)
print(func.get_summary(recommendation))
from nevergrad.optimization import optimizerlib
def square(x):
return (x - 0.75)**2
optimizer = optimizerlib.OnePlusOne(dimension=1, budget=100, num_workers=5)
recommendation = optimizer.optimize(square, executor=None, batch_mode=True)
print(recommendation)
