Add `'total_gain'` and `'total_cover'` as possible `importance_type` arguments to `Booster.get_score` in the Python package. `get_score` already accepts a `'gain'` argument, which returns each feature's average gain over all of its splits. `'total_gain'` does the same, but returns a total rather than an average. This seems more intuitively meaningful, and also matches the behavior of the R package's `xgb.importance` function. I also added an analogous `'total_cover'` command for consistency. This should resolve #3484.
This folder contains testcases for xgboost.