'Fit the model from data in X and transform X.
Parameters
X : array-like, shape (n_samples, n_features)
Training vector, where n_samples in the number of samples
and n_features is the number of features.
Returns
X_new : array-like, shape (n_samples, n_components)'

def fit_transform(self, X, y=None, **params):

    self.fit(X, **params)
    X_transformed = (self.alphas_ * np.sqrt(self.lambdas_))
    if self.fit_inverse_transform:
        self._fit_inverse_transform(X_transformed, X)
    return X_transformed
