def __init__(self,
experiment_name,
experiment,
parameter_config,
X_train,
y_train,
logger=None):
self.logger = logger if (logger is not None) else get_logger()
self._experiment_name = experiment_name
self._experiment = experiment
self._goal = self._experiment.spec.objective.type
self._dim = parameter_config.dim
self._lowerbound = parameter_config.lower_bounds
self._upperbound = parameter_config.upper_bounds
self._types = parameter_config.parameter_types
self._names = parameter_config.names
# record all the feasible values of discrete type variables
self._discrete_info = parameter_config.discrete_info
self._categorical_info = parameter_config.categorical_info
self._name_id = parameter_config.name_ids
self._X_train = self._mapping_params(X_train)
self.parse_X()
self._y_train = y_train
self._parse_metric()
def __init__(self,
N,
l,
u,
scaler,
X_train,
y_train,
current_optimal,
experiment_name,
mode,
trade_off,
length_scale,
noise,
nu,
kernel_type,
n_estimators,
max_features,
model_type,
logger=None):
self.logger = logger if (logger is not None) else get_logger()
self.N = N
self.l = l
self.u = u
self.scaler = scaler
self.buckets = []
self.dim = None
self._experiment_name = experiment_name
if model_type == "gp":
model = GaussianProcessModel(
length_scale=length_scale,
noise=noise,
nu=nu,
kernel_type=kernel_type,
)
else:
model = RandomForestModel(
n_estimators=n_estimators,
max_features=max_features,
)
self.logger.debug("before model fit",
extra={"Experiment": self._experiment_name})
model.fit(X_train, y_train)
self.logger.debug("after model fit",
extra={"Experiment": self._experiment_name})
self.aq_func = AcquisitionFunc(
model=model,
current_optimal=current_optimal,
mode=mode,
trade_off=trade_off,
)