boosting_type "rf" leads to unresolvable failures

[namato]

Hey guys, I'm doing some grid searching with various boosting_type settings, and notice that whenever I use boosting_type rf, the fit fails:

[LightGBM] [Fatal] Check failed: config->bagging_freq > 0 && config->bagging_fraction < 1.0f && config-
>bagging_fraction > 0.0f at /home/travis/build/Microsoft/LightGBM/python-package/compile/src/boosting/r
f.hpp, line 29 .

Even though in the sklearn API:

1. There are no other parameters set other than the defaults
2. The fit succeeds with "gbd", "dart" and "goss"
3. There is no way to actually set these values in the sklearn API (bagging_freq, bagging_fraction) other than through additional kwargs, which is apparently unsupported

[guolinke]

for random forest, you should use subsets (that is the definition of RF). As a result, you should set baggeng_freq=1 with bagging_fraction < 1.0f