The models are good but they do not predict "optimality".  Will need to use them to predict the graph at future time intervals that I can then run shortest path and compare to the test path.

Added some extra code for parameter tuning and graph prediction.
Tweaked models per metrics tests on lambda and step size.
Seeing roughly a 29% edge existence error rate when comparing predicted graph 16 against 15.  (A better comparison would be predicted 15 against actual 15, doing that next.)
Okay this approach to prediction - predicting entire future graphs - yields error metrics will assist in tuning the existence model which currently needs help.  Its still not perfect because the prediction only considers the existence of edges we have seen in the past because it takes too long to predict n^2 edges for all vertices, but its a good start.

After that we need something like Dijkstra to walk the predicted graph to answer the question of optimality posed by the Kagggle competition, but that won't be done before the poster session tomorrow.
Repackage some code since it was getting a bit out of hand.
About to run parameter tuning on graph prediction.  Checking in refactoring needed to make that happen.