class AdaBoostClassifierImpl():
def __init__(self,
base_estimator=None,
n_estimators=50,
learning_rate=1.0,
algorithm='SAMME.R',
random_state=None):
self._hyperparams = {
'base_estimator': base_estimator,
'n_estimators': n_estimators,
'learning_rate': learning_rate,
'algorithm': algorithm,
'random_state': random_state
}
self._wrapped_model = SKLModel(**self._hyperparams)
def fit(self, X, y=None):
if (y is not None):
self._wrapped_model.fit(X, y)
else:
self._wrapped_model.fit(X)
return self
def predict(self, X):
return self._wrapped_model.predict(X)
def predict_proba(self, X):
return self._wrapped_model.predict_proba(X)
def decision_function(self, X):
return self._wrapped_model.decision_function(X)
class AdaBoostClassifierImpl():
def __init__(self, base_estimator=None, n_estimators=50, learning_rate=1.0, algorithm='SAMME.R', random_state=None):
if isinstance(base_estimator, lale.operators.Operator):
if isinstance(base_estimator, lale.operators.IndividualOp):
base_estimator = base_estimator._impl_instance()._wrapped_model
else:
raise ValueError("If base_estimator is a Lale operator, it needs to be an individual operator. ")
self._hyperparams = {
'base_estimator': base_estimator,
'n_estimators': n_estimators,
'learning_rate': learning_rate,
'algorithm': algorithm,
'random_state': random_state}
self._wrapped_model = SKLModel(**self._hyperparams)
def fit(self, X, y=None):
if (y is not None):
self._wrapped_model.fit(X, y)
else:
self._wrapped_model.fit(X)
return self
def predict(self, X):
return self._wrapped_model.predict(X)
def predict_proba(self, X):
return self._wrapped_model.predict_proba(X)
def decision_function(self, X):
return self._wrapped_model.decision_function(X)
