Hey, Our ML ETL pipeline has several complex steps that I’d like to address with custom Transformers in an ML Pipeline. Looking at the Tokenizer and HashingTF transformers I see these handy traits (HasInputCol, HasLabelCol, HasOutputCol, etc.) but they have strict access modifiers. How can I use these with custom Transformer/Estimator implementations?
I’m stuck depositing my implementations in org.apache.spark.ml, which is tolerable for now, but I’m wondering if I’m missing some pattern? Thanks, mn --------------------------------------------------------------------- To unsubscribe, e-mail: user-unsubscr...@spark.apache.org For additional commands, e-mail: user-h...@spark.apache.org
