Add prediction of feature contributions (#2003)

* Add prediction of feature contributions

This implements the idea described at http://blog.datadive.net/interpreting-random-forests/
which tries to give insight in how a prediction is composed of its feature contributions
and a bias.

* Support multi-class models

* Calculate learning_rate per-tree instead of using the one from the first tree

* Do not rely on node.base_weight * learning_rate having the same value as the node mean value (aka leaf value, if it were a leaf); instead calculate them (lazily) on-the-fly

* Add simple test for contributions feature

* Check against param.num_nodes instead of checking for non-zero length

* Loop over all roots instead of only the first

include/xgboost/c_api.h

@@ -382,6 +382,7 @@ XGB_DLL int XGBoosterEvalOneIter(BoosterHandle handle,
  *          0:normal prediction
  *          1:output margin instead of transformed value
  *          2:output leaf index of trees instead of leaf value, note leaf index is unique per tree
+ *          4:output feature contributions of all trees instead of predictions
  * \param ntree_limit limit number of trees used for prediction, this is only valid for boosted trees
  *    when the parameter is set to 0, we will use all the trees
  * \param out_len used to store length of returning result