def fit(self, X, y=None): self._sklearn_model = SKLModel(**self._hyperparams) if (y is not None): self._sklearn_model.fit(X, y) else: self._sklearn_model.fit(X) return self
def __init__(self, base_estimator=None, n_estimators=50, learning_rate=1.0, loss='linear', random_state=None): self._hyperparams = { 'base_estimator': base_estimator, 'n_estimators': n_estimators, 'learning_rate': learning_rate, 'loss': loss, 'random_state': random_state} self._wrapped_model = SKLModel(**self._hyperparams)
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 __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)