def __init__(
self,
loss="hinge",
penalty="l2",
alpha=0.0001,
l1_ratio=0.15,
fit_intercept=True,
max_iter=None,
tol=None,
shuffle=True,
verbose=0,
epsilon=0.1,
n_jobs=None,
random_state=None,
learning_rate="optimal",
eta0=0.0,
power_t=0.5,
early_stopping=False,
validation_fraction=0.1,
n_iter_no_change=5,
class_weight="balanced",
warm_start=False,
average=False,
):
self._hyperparams = {
"loss": loss,
"penalty": penalty,
"alpha": alpha,
"l1_ratio": l1_ratio,
"fit_intercept": fit_intercept,
"max_iter": max_iter,
"tol": tol,
"shuffle": shuffle,
"verbose": verbose,
"epsilon": epsilon,
"n_jobs": n_jobs,
"random_state": random_state,
"learning_rate": learning_rate,
"eta0": eta0,
"power_t": power_t,
"early_stopping": early_stopping,
"validation_fraction": validation_fraction,
"n_iter_no_change": n_iter_no_change,
"class_weight": class_weight,
"warm_start": warm_start,
"average": average,
}
self._wrapped_model = SKLModel(**self._hyperparams)
def __init__(self,
loss='hinge',
penalty='l2',
alpha=0.0001,
l1_ratio=0.15,
fit_intercept=True,
max_iter=None,
tol=None,
shuffle=True,
verbose=0,
epsilon=0.1,
n_jobs=None,
random_state=None,
learning_rate='optimal',
eta0=0.0,
power_t=0.5,
early_stopping=False,
validation_fraction=0.1,
n_iter_no_change=5,
class_weight='balanced',
warm_start=False,
average=False):
self._hyperparams = {
'loss': loss,
'penalty': penalty,
'alpha': alpha,
'l1_ratio': l1_ratio,
'fit_intercept': fit_intercept,
'max_iter': max_iter,
'tol': tol,
'shuffle': shuffle,
'verbose': verbose,
'epsilon': epsilon,
'n_jobs': n_jobs,
'random_state': random_state,
'learning_rate': learning_rate,
'eta0': eta0,
'power_t': power_t,
'early_stopping': early_stopping,
'validation_fraction': validation_fraction,
'n_iter_no_change': n_iter_no_change,
'class_weight': class_weight,
'warm_start': warm_start,
'average': average
}
self._wrapped_model = SKLModel(**self._hyperparams)
def partial_fit(self, X, y=None, classes=None):
if not hasattr(self, "_wrapped_model"):
self._wrapped_model = SKLModel(**self._hyperparams)
self._wrapped_model.partial_fit(X, y, classes=classes)
return self