Yep, I definitely agree with you. Moreover, such improvement should reduce parallel hierarchies in trainers and preprocessors, from this point of view preprocessor will be equal to a trainer. In my opinion, this improvement is very important for ml module because it can give a flexible hierarchy of components.
I created a ticket for serializable object support in Vectors: https://issues.apache.org/jira/browse/IGNITE-11647 Another related ticket to this thread: https://issues.apache.org/jira/browse/IGNITE-11642 чт, 28 мар. 2019 г. в 11:27, Alexey Zinoviev <zaleslaw....@gmail.com>: > Hi, Igniters > > The new functionality of building Vectors was merged to Apache Ignite in > the > next commit > < > https://github.com/apache/ignite/commit/a0a15d62a250defb0db9ec72153ee287830f6a15> > > > This new functionality brings to Ignite ML the new approach of building > vectors. But in my opinion the shouldn't constrain ourselves with narrow > understanding of Vector nature as an analogue of double[] array. > > I suggest to extend the Vector and Vectorizer API to support Strings and > another types (like Blobs, Images and etc) as a vector elements. > > It brings next advantages: > * gives a chance to inify the hierarchy of Preprocessing Trainers and Model > Trainers > * give us a chance to implement ML algorithms working not only with doubles > * unifies our Vectorizers as a first step in our Pipelines > * drops a lot of unused generics > * makes one simple requirement to final users: convert their data to > Vectors > > Join to discussion, ML-interested persons and share your opinon here! > > > > -- > Sent from: http://apache-ignite-developers.2346864.n4.nabble.com/ >
