shingjan commented on code in PR #12144:
URL: https://github.com/apache/tvm/pull/12144#discussion_r926281877
##########
python/tvm/auto_scheduler/cost_model/xgb_model.py:
##########
@@ -539,125 +539,128 @@ def feval(preds, labels):
return feval
-def custom_callback(
- stopping_rounds,
- metric,
- fevals,
- evals=(),
- log_file=None,
- maximize=False,
- verbose_eval=True,
- skip_every=2,
-):
- """Callback function for xgboost to support multiple custom evaluation
functions"""
- # pylint: disable=import-outside-toplevel
- from xgboost.core import EarlyStopException
- from xgboost.callback import _fmt_metric
-
- try:
- from xgboost.training import aggcv
- except ImportError:
- from xgboost.callback import _aggcv as aggcv
-
- state = {}
- metric_shortname = metric.split("-")[1]
-
- def init(env):
- """internal function"""
- bst = env.model
-
- state["maximize_score"] = maximize
- state["best_iteration"] = 0
- if maximize:
- state["best_score"] = float("-inf")
- else:
- state["best_score"] = float("inf")
+class CustomCallback(callback.TrainingCallback):
Review Comment:
`TrainingCallBack` could have different implementation and backwards
compatibility could be very useful here in case `xgboost` < 1.6.0 is used. You
can refer to the change [here](https://github.com/apache/tvm/pull/12141) in
meta schedule.
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