def fetch_predict_estimator(task_type,
config,
X_train,
y_train,
weight_balance=0,
data_balance=0,
combined=False):
# Build the ML estimator.
from solnml.components.utils.balancing import get_weights, smote
_fit_params = {}
config_dict = config.get_dictionary().copy()
if weight_balance == 1:
_init_params, _fit_params = get_weights(y_train, config['estimator'],
None, {}, {})
for key, val in _init_params.items():
config_dict[key] = val
if data_balance == 1:
X_train, y_train = smote(X_train, y_train)
if task_type in CLS_TASKS:
if combined:
from solnml.utils.combined_evaluator import get_estimator
else:
from solnml.components.evaluators.cls_evaluator import get_estimator
else:
from solnml.components.evaluators.reg_evaluator import get_estimator
_, estimator = get_estimator(config_dict)
estimator.fit(X_train, y_train, **_fit_params)
return estimator
def fetch_predict_estimator(task_type,
estimator_id,
config,
X_train,
y_train,
weight_balance=0,
data_balance=0):
# Build the ML estimator.
from solnml.components.utils.balancing import get_weights, smote
_fit_params = {}
config_dict = config.copy()
if weight_balance == 1:
_init_params, _fit_params = get_weights(y_train, estimator_id, None,
{}, {})
for key, val in _init_params.items():
config_dict[key] = val
if data_balance == 1:
X_train, y_train = smote(X_train, y_train)
if task_type in CLS_TASKS:
from solnml.components.evaluators.cls_evaluator import get_estimator
elif task_type in RGS_TASKS:
from solnml.components.evaluators.rgs_evaluator import get_estimator
_, estimator = get_estimator(config_dict, estimator_id)
estimator.fit(X_train, y_train, **_fit_params)
return estimator
def get_fit_params(self, y, estimator):
from solnml.components.utils.balancing import get_weights
_init_params, _fit_params = get_weights(y, estimator, None, {}, {})
return _init_params, _fit_params