I'm learning how to use pyspark, and I'm wondering if it has any ways to implement positive-unlabeled learning? From searching this question I haven't been able to find any examples specific in spark for python (only java which I am not familar with).
I'm looking to do positive-unlabeled machine learning that has the potential to scale, so whilst I can get PU-learning running in packages focused on scikit-learn models for this I want to know if it would be possible to do in PySpark.
Currently not sure where to start/look to answer this question. I've been looking in the spark docs (https://spark.apache.org/docs/latest/api/python/reference/pyspark.ml.html#classification) and I see they offer models that can do binary classification. I'm still learning about machine learning, so I'm wondering if it would be possible for me to use a binary classifier but somehow re-weigh the negative class so it's more like it's unlabelled vs positive?