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import time
import random
from bayes_opt import BayesianOptimization
from bayes_opt.util import UtilityFunction
import asyncio
import threading
from colorama import Fore
try:
import json
import tornado.ioloop
import tornado.httpserver
from tornado.web import RequestHandler
import requests
except ImportError:
raise ImportError(
"In order to run this example you must have the libraries: " +
"`tornado` and `requests` installed."
)
def black_box_function(x, y):
"""Function with unknown internals we wish to maximize.
This is just serving as an example, however, for all intents and
purposes think of the internals of this function, i.e.: the process
which generates its outputs values, as unknown.
"""
time.sleep(random.randint(1, 7))
return -x ** 2 - (y - 1) ** 2 + 1
class BayesianOptimizationHandler(RequestHandler):
"""Basic functionality for NLP handlers."""
_bo = BayesianOptimization(
f=black_box_function,
pbounds={"x": (-4, 4), "y": (-3, 3)}
)
_uf = UtilityFunction(kind="ucb", kappa=3, xi=1)
def post(self):
"""Deal with incoming requests."""
body = tornado.escape.json_decode(self.request.body)
try:
self._bo.register(
params=body["params"],
target=body["target"],
)
print("BO has registered: {} points.".format(len(self._bo.space)), end="\n\n")
except KeyError:
pass
finally:
suggested_params = self._bo.suggest(self._uf)
self.write(json.dumps(suggested_params))
def run_optimization_app():
asyncio.set_event_loop(asyncio.new_event_loop())
handlers = [
(r"/bayesian_optimization", BayesianOptimizationHandler),
]
server = tornado.httpserver.HTTPServer(
tornado.web.Application(handlers)
)
server.listen(9009)
tornado.ioloop.IOLoop.instance().start()
def run_optimizer():
global optimizers_config
config = optimizers_config.pop()
name = config["name"]
colour = config["colour"]
register_data = {}
max_target = None
for _ in range(10):
status = name + " wants to register: {}.\n".format(register_data)
resp = requests.post(
url="http://localhost:9009/bayesian_optimization",
json=register_data,
).json()
target = black_box_function(**resp)
register_data = {
"params": resp,
"target": target,
}
if max_target is None or target > max_target:
max_target = target
status += name + " got {} as target.\n".format(target)
status += name + " will to register next: {}.\n".format(register_data)
print(colour + status, end="\n")
global results
results.append((name, max_target))
print(colour + name + " is done!", end="\n\n")
if __name__ == "__main__":
ioloop = tornado.ioloop.IOLoop.instance()
optimizers_config = [
{"name": "optimizer 1", "colour": Fore.RED},
{"name": "optimizer 2", "colour": Fore.GREEN},
{"name": "optimizer 3", "colour": Fore.BLUE},
]
app_thread = threading.Thread(target=run_optimization_app)
app_thread.daemon = True
app_thread.start()
targets = (
run_optimizer,
run_optimizer,
run_optimizer
)
optimizer_threads = []
for target in targets:
optimizer_threads.append(threading.Thread(target=target))
optimizer_threads[-1].daemon = True
optimizer_threads[-1].start()
results = []
for optimizer_thread in optimizer_threads:
optimizer_thread.join()
for result in results:
print(result[0], "found a maximum value of: {}".format(result[1]))
ioloop.stop()
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