Is anyone aware of a scikit-compatible network Lasso (nLasso) implementation?
These papers have source code as well:
 M.Yamada, T. Koh, T. Iwata, J. Shawe-Taylor, and S. Kaski, “Localized Lasso for High-Dimensional Regression,” in Proceedings of the 20th International Conference on Artificial Intelligence and Statistics, vol. 54. Fort Lauderdale, FL, USA: PMLR, Apr. 2017, pp. 325–333.
But these particular implementations are not very general and can't be used as a part of the pipelines.
I would like to use nLasso to do something like this (the example picked from the scikit tutorial):
import matplotlib.pyplot as plt import numpy as np from sklearn.datasets import load_boston from sklearn.feature_selection import SelectFromModel from sklearn.linear_model import LassoCV # Load the boston dataset. boston = load_boston() X, y = boston['data'], boston['target'] # We use the base estimator LassoCV since the L1 norm promotes sparsity of features. clf = LassoCV(cv=5) # Set a minimum threshold of 0.25 sfm = SelectFromModel(clf, threshold=0.25) sfm.fit(X, y) n_features = sfm.transform(X).shape