def optimize(scenario, run, forest=False, seed=8, ratio=0.8):
types, bounds = get_types(scenario.cs, scenario.feature_array)
rfr = RandomForestWithInstances(types=types, bounds=bounds, instance_features=scenario.feature_array, seed=seed)
ei = EI(model=rfr)
if forest:
optimizer = ForestSearch(ei, scenario.cs, ratio=ratio)
else:
optimizer = InterleavedLocalAndRandomSearch(ei, scenario.cs)
scenario.output_dir = "%s_%s_%d_%lf" % ("./logs/run_", "forest_" if forest else "random_", seed, time.time())
smac = SMAC(
scenario=scenario,
rng=np.random.RandomState(seed),
model=rfr,
acquisition_function=ei,
acquisition_function_optimizer=optimizer,
tae_runner=run,
)
try:
incumbent = smac.optimize()
finally:
incumbent = smac.solver.incumbent
return smac.get_tae_runner().run(incumbent, 1)[1]
def test_get_runhistory_and_trajectory_and_tae_runner(self):
ta = ExecuteTAFuncDict(lambda x: x**2)
smac = SMAC(tae_runner=ta, scenario=self.scenario)
self.assertRaises(ValueError, smac.get_runhistory)
self.assertRaises(ValueError, smac.get_trajectory)
smac.trajectory = 'dummy'
self.assertEqual(smac.get_trajectory(), 'dummy')
smac.runhistory = 'dummy'
self.assertEqual(smac.get_runhistory(), 'dummy')
self.assertEqual(smac.get_tae_runner(), ta)
def bayesian_optimize(self):
"""conduct Bayesian optimization on the hyperparameters, starting at current values"""
if self.algorithm in ['GNB','Perceptron']:
return self
else:
cs = ConfigurationSpace()
cs.add_hyperparameters(list(getattr(util, self.algorithm + '_range')(self.hyperparameters).values()))
if self.algorithm == 'kNN':
if self.hyperparameters['k'] == 1: num = 3
else: num = 5
else: num = 10
scenario = Scenario({'run_obj': 'quality', 'runcount-limit': num, 'cs': cs, 'deterministic': 'true', 'memory_limit': None})
smac = SMAC(scenario=scenario, rng=np.random.RandomState(100), tae_runner=self.error_function)
try:
incumbent = smac.optimize()
finally:
incumbent = smac.solver.incumbent
self.error = smac.get_tae_runner().run(incumbent, 1)[1]
self.hyperparameters = incumbent.get_dictionary()
self.bayesian_optimized = True
return self
scenario = Scenario({"run_obj": "quality", # we optimize quality (alternative runtime)
"runcount-limit": 50, # maximum number of function evaluations
"cs": cs, # configuration space
"deterministic": "true",
"memory_limit": 3072, # adapt this to reasonable value for your hardware
"shared_model": True,
"input_psmac_dirs": "smac3-output*"
})
# To optimize, we pass the function to the SMAC-object
smac = SMAC(scenario=scenario, rng=np.random.RandomState(42),
tae_runner=rf_from_cfg)
# Example call of the function with default values
# It returns: Status, Cost, Runtime, Additional Infos
def_value = smac.get_tae_runner().run(cs.get_default_configuration(), 1)[1]
print("Value for default configuration: %.2f" % (def_value))
# Start optimization
try:
incumbent = smac.optimize()
finally:
incumbent = smac.solver.incumbent
incumbent._values
mlflow.log_param("city",city)
mlflow.log_param("model","rf")
all_feats = ""
for elem in features:
if all_feats == "":
