def __init__(self, method='yeo-johnson', standardize=True, copy=True):
self._hyperparams = {
'method': method,
'standardize': standardize,
'copy': copy
}
self._wrapped_model = SKLModel(**self._hyperparams)
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,
with_centering=True,
with_scaling=True,
quantile_range=(25.0, 75.0),
copy=True):
self._hyperparams = {
'with_centering': with_centering,
'with_scaling': with_scaling,
'quantile_range': quantile_range,
'copy': copy
}
self._wrapped_model = SKLModel(**self._hyperparams)
def __init__(self,
n_quantiles=1000,
output_distribution='uniform',
ignore_implicit_zeros=False,
subsample=100000,
random_state=None,
copy=True):
self._hyperparams = {
'n_quantiles': n_quantiles,
'output_distribution': output_distribution,
'ignore_implicit_zeros': ignore_implicit_zeros,
'subsample': subsample,
'random_state': random_state,
'copy': copy
}
self._wrapped_model = SKLModel(**self._hyperparams)
def __init__(self, threshold=0.0, copy=True):
self._hyperparams = {'threshold': threshold, 'copy': copy}
self._wrapped_model = SKLModel(**self._hyperparams)
def __init__(self, norm='l2', copy=True):
self._hyperparams = {'norm': norm, 'copy': copy}
self._wrapped_model = SKLModel(**self._hyperparams)
def __init__(self, copy=True):
self._hyperparams = {'copy': copy}
self._wrapped_model = SKLModel(**self._hyperparams)
def __init__(self, degree=2, interaction_only=False, include_bias=True):
self._hyperparams = {
'degree': degree,
'interaction_only': interaction_only,
'include_bias': include_bias}
self._wrapped_model = SKLModel(**self._hyperparams)
def __init__(self, feature_range=(0, 1), copy=True):
self._hyperparams = {
'feature_range': feature_range,
'copy': copy}
self._wrapped_model = SKLModel(**self._hyperparams)