Upgrade xgboost dependency to be 1.6.1 version #822
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| @@ -33,6 +33,8 @@ class XGBoostClassificationModelParitySpec extends SparkParityBase { | |||
| // These params are not needed for making predictions, so we don't serialize them | |||
| override val unserializedParams = Set("labelCol", "evalMetric", "objective") | |||
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| override val excludedColsForComparison = Array[String]("prediction") | |||
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Exclude this column when comparing result.
The reason is xgboost might generate some prediction result with equal probability ( 0.5/0.5 ) on each label, then the prediction label might be nondeterministic 0 or 1.
So I exclude the prediction column.
But, we still compare the "probability" column which can ensure the model inference parity.
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If we set the random seed can we get things to be deterministic for the test?
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Seemingly no. There're some other non-deterministic factors such as multiple spark tasks run in parallelism and causes different sum order when aggregation.
Assuming it works, I still don't recommend the approach because next time xgboost upgrading we might need to update test again.
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CC @jsleight Could you help review and merge if it is good ? |
Signed-off-by: Weichen Xu weichen.xu@databricks.com
Upgrade xgboost dependency to be 1.6.1 version