Dealing with bipartite/monopartite datasets can be improved

[pedroilidio]

In different scenarios, we sometimes deal with bipartite formatted data, but sometimes the bipartite datasets are converted to the monopartite form, monopartite meaning that X is formed by pairwise concatenations of all possible X[0] and X[1] rows, and bipartite meaning X = [X[0], X[1]].

As mentioned in #5 (comment), the way of distinguishing between these two formats deserves more careful solutions than what we currently do:

bipartite_learn/bipartite_learn/utils/__init__.py

Lines 40 to 42 in 8499867

	def _X_is_multipartite(X):
	# TODO: find a better way of deciding.
	return isinstance(X, (tuple, list))

Even more so since some estimators do accept both types of input for predict() (tree-based models in general) while others only accept the bipartite format (the matrix factorization ones, for instance), but all of them should yield flattened predictions for better integration with scikit-learn scoring utilities, which I reckon can be quite confusing.

1. I suppose an estimator tag would be an appropriate way of signaling that.
2. Maybe a whole Dataset class would facilitate maintenance in the long term.