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License: Apache License 2.0
Positive-Unlabeled Learning for Apache Spark
License: Apache License 2.0
I've created a fork of this project to upgrade to the latest version of Spark and make a few other changes. However, I'm having problems understanding the simple use-case of working with this in order to create unit tests:
https://github.com/darkfrog26/pu4spark/blob/master/src/test/scala/specs/SimpleUsageSpec.scala#L24
Any feedback on proper operation of would be appreciated. I'm still a bit new to Spark ML, but your README gives very little clarity on proper operation.
In every iterator, you use StringIndexer to rebuild label index and call method of getPOne to get positive sample probability. But, 0 always stands for the most frequent label after StringIndexer . Maybe I misunderstand somewhere?
It looks like in review of the source files, they include both a copyright statement and an apache license. Apache states that while committers retain copyright, they ask that committers do not add copyright statements:
http://www.apache.org/dev/apply-license.html#contributor-copyright
Could these copyright clauses be removed?
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