def __init__(
self,
base_estimator=None,
n_estimators=10,
max_samples=1.0,
max_features=1.0,
bootstrap=True,
bootstrap_features=False,
oob_score=False,
warm_start=False,
n_jobs=None,
random_state=None,
verbose=0,
):
self._hyperparams = {
"base_estimator": make_sklearn_compat(base_estimator),
"n_estimators": n_estimators,
"max_samples": max_samples,
"max_features": max_features,
"bootstrap": bootstrap,
"bootstrap_features": bootstrap_features,
"oob_score": oob_score,
"warm_start": warm_start,
"n_jobs": n_jobs,
"random_state": random_state,
"verbose": verbose,
}
self._wrapped_model = SKLModel(**self._hyperparams)
def __init__(self,
base_estimator=None,
n_estimators=10,
max_samples=1.0,
max_features=1.0,
bootstrap=True,
bootstrap_features=False,
oob_score=False,
warm_start=False,
n_jobs=None,
random_state=None,
verbose=0):
self._hyperparams = {
'base_estimator': make_sklearn_compat(base_estimator),
'n_estimators': n_estimators,
'max_samples': max_samples,
'max_features': max_features,
'bootstrap': bootstrap,
'bootstrap_features': bootstrap_features,
'oob_score': oob_score,
'warm_start': warm_start,
'n_jobs': n_jobs,
'random_state': random_state,
'verbose': verbose
}
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,
):
estimator_impl = base_estimator
if isinstance(estimator_impl, lale.operators.Operator):
if isinstance(estimator_impl, lale.operators.IndividualOp):
estimator_impl = estimator_impl._impl_instance()
wrapped_model = getattr(estimator_impl, "_wrapped_model", None)
if wrapped_model is not None:
estimator_impl = wrapped_model
else:
raise ValueError(
"If base_estimator is a Lale operator, it needs to be an individual operator. "
)
self._hyperparams = {
"base_estimator": estimator_impl,
"n_estimators": n_estimators,
"learning_rate": learning_rate,
"algorithm": algorithm,
"random_state": random_state,
}
self._wrapped_model = SKLModel(**self._hyperparams)
self._hyperparams["base_estimator"] = base_estimator
def __init__(
self,
base_estimator=None,
n_estimators=50,
learning_rate=1.0,
loss="linear",
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 = getattr(base_estimator, "_wrapped_model", None)
if wrapped_model is not None:
base_estimator = 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,
"loss": loss,
"random_state": random_state,
}
self._wrapped_model = SKLModel(**self._hyperparams)
def fit(self, X, y, sample_weight=None):
if isinstance(X, pd.DataFrame):
feature_transformer = FunctionTransformer(
func=lambda X_prime: pd.DataFrame(X_prime, columns=X.columns),
inverse_func=None,
check_inverse=False,
)
self._hyperparams["base_estimator"] = (
feature_transformer >> self._hyperparams["base_estimator"])
self._wrapped_model = SKLModel(**self._hyperparams)
self._wrapped_model.fit(X, y, sample_weight)
return self
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,
estimators=None,
voting='hard',
weights=None,
n_jobs=None,
flatten_transform=True):
self._hyperparams = {
'estimators': estimators,
'voting': voting,
'weights': weights,
'n_jobs': n_jobs,
'flatten_transform': flatten_transform
}
self._wrapped_model = SKLModel(**self._hyperparams)
def fit(self, X, y=None):
if isinstance(X, pd.DataFrame):
feature_transformer = FunctionTransformer(
func=lambda X_prime: pd.DataFrame(X_prime, columns=X.columns),
inverse_func=None,
check_inverse=False,
)
self._hyperparams["base_estimator"] = _FitSpecProxy(
feature_transformer >> self._hyperparams["base_estimator"])
self._wrapped_model = SKLModel(**self._hyperparams)
if y is not None:
self._wrapped_model.fit(X, y)
else:
self._wrapped_model.fit(X)
return self
def __init__(
self,
base_estimator=None,
*,
n_estimators=50,
learning_rate=1.0,
algorithm="SAMME.R",
random_state=None,
):
if base_estimator is None:
estimator_impl = None
else:
estimator_impl = _FitSpecProxy(base_estimator)
self._hyperparams = {
"base_estimator": estimator_impl,
"n_estimators": n_estimators,
"learning_rate": learning_rate,
"algorithm": algorithm,
"random_state": random_state,
}
self._wrapped_model = SKLModel(**self._hyperparams)
self._hyperparams["base_estimator"] = base_estimator