Responding to my own question in case someone else has a similar
question: The solution is to use BinaryObjectVectorizer<K> which gets
the labeled features from the binary object!
*Question:* Why is BinaryObjectVectorizer a final class? It would be
convenient to be able to extend it for specific subclasses on
pre-defined features.
On 20.06.22 13:22, Thomas Kramer wrote:
I am storing my ML training data in a cache with binary data:
IgniteCache<UUID, BinaryObject> cache =
ignite.cache("cache").withKeepBinary();
I can't seem to understand how to use the Ignite ML classes like
Preprocessor and DatasetBuilder with this cache? Do I first have to
create another IgniteCache<UUID, MyClass> or Map<UUID, MyClass> and copy
the data from the original cache?
Should I better not use BinaryObject for ML caches? Or would I write my
custom vectorizer that creates MyClass instances of the binary objects?
