A Python implementation of Attribute-based Decision Graph and a method for missing-data imputation using it
[1] João Roberto Bertini Junior, Maria do Carmo Nicoletti, Liang Zhao, "An embedded imputation method via Attribute-based Decision Graphs", Expert Systems with Applications, Volume 57, 2016, Pages 159-177, doi: http://dx.doi.org/10.1016/j.eswa.2016.03.027. http://www.sciencedirect.com/science/article/pii/S0957417416301208
[2] João Roberto Bertini, Maria do Carmo Nicoletti, Liang Zhao, "Attribute-based Decision Graphs: A framework for multiclass data classification", Neural Networks, Volume 85, 2017, Pages 69-84, http://dx.doi.org/10.1016/j.neunet.2016.09.008. http://www.sciencedirect.com/science/article/pii/S0893608016301381
[3] Mikhail Belkin, Partha Niyogi, and Vikas Sindhwani, "Manifold Regularization: A Geometric Framework for Learning from Labeled and Unlabeled Examples", J. Mach. Learn. Res. 7 (December 2006), 2399-2434. http://www.jmlr.org/papers/v7/belkin06a.html
Requirements:
>>> import numpy as np
>>> from abdg import ABDGImput
>>> from sklearn.datasets import make_classification
>>>
>>> X, y = make_classification(n_samples=2000, n_features=7, n_redundant=2,
>>> n_informative=4, n_classes=3)
>>>
>>> idx = np.random.choice(np.arange(X.shape[0]), X.shape[0]//2)
>>> X[idx, 0] = np.nan
>>>
>>> abdg = ABDGImput(categorical_features='auto', n_iter=4, alpha=0.6, L=0.5,
>>> sampling='normal', update_step=100, random_state=None)
>>> abdg.fit(X, y)
>>>
>>> X_imp, y_imp = abdg.predict(X, y)