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,
n_components=None,
init=None,
solver='cd',
beta_loss='frobenius',
tol=0.0001,
max_iter=200,
random_state=None,
alpha=0.0,
l1_ratio=0.0,
verbose=0,
shuffle=False):
self._hyperparams = {
'n_components': n_components,
'init': init,
'solver': solver,
'beta_loss': beta_loss,
'tol': tol,
'max_iter': max_iter,
'random_state': random_state,
'alpha': alpha,
'l1_ratio': l1_ratio,
'verbose': verbose,
'shuffle': shuffle
}
self._wrapped_model = SKLModel(**self._hyperparams)
def __init__(
self,
n_components=None,
init=None,
solver="cd",
beta_loss="frobenius",
tol=0.0001,
max_iter=200,
random_state=None,
alpha=0.0,
l1_ratio=0.0,
verbose=0,
shuffle=False,
):
self._hyperparams = {
"n_components": n_components,
"init": init,
"solver": solver,
"beta_loss": beta_loss,
"tol": tol,
"max_iter": max_iter,
"random_state": random_state,
"alpha": alpha,
"l1_ratio": l1_ratio,
"verbose": verbose,
"shuffle": shuffle,
}
self._wrapped_model = SKLModel(**self._hyperparams)
def __init__(self, **hyperparams):
self._hyperparams = hyperparams
self._wrapped_model = SKLModel(**self._hyperparams)