def _assemble_multi_class_output(self, trees):
# Multi-class output is calculated based on discussion in
# https://github.com/dmlc/xgboost/issues/1746#issuecomment-295962863
splits = _split_trees_by_classes(trees, self._output_size)
base_score = self._base_score
exprs = [self._assemble_single_output(t, base_score) for t in splits]
proba_exprs = utils.softmax_exprs(exprs)
return ast.VectorVal(proba_exprs)
def _assemble_multi_class_output(self, estimator_params):
# Multi-class output is calculated based on discussion in
# https://github.com/dmlc/xgboost/issues/1746#issuecomment-295962863
splits = _split_estimator_params_by_classes(
estimator_params, self._output_size)
base_score = self._base_score
exprs = [
self._assemble_single_output(e, base_score=base_score, split_idx=i)
for i, e in enumerate(splits)
]
proba_exprs = utils.softmax_exprs(exprs)
return ast.VectorVal(proba_exprs)