Ken, thanks for the feedback, part of the ideas looks like the good candidates for the next release for the Java API.
We should understand, that Python API could only wrap Java API. We approach with wrapping via P4j library as in mentioned repository could be used, it is a common approach, it is used in PySparj, for example. Currently I m not ready to make Python wrapper a part of Ignite for many reasons: part of ML API is released firstly, this is a big work for many Committer, we couldnt guarantee the release cycle for such component. пт, 6 мар. 2020 г., 2:49 Denis Magda <dma...@apache.org>: > Folks, > > Does it make sense to take an approach of Python ML implementation > available for GridGain in a beta mode? (where Python APIs wrap around Java > ML library) > > https://www.gridgain.com/docs/latest/developers-guide/python-ml/using-python-ml > > - > Denis > > > On Thu, Mar 5, 2020 at 6:50 AM Alexey Zinoviev <zaleslaw....@gmail.com> > wrote: > > > Agree with simple case, I think we could start from the simple poc for > the > > Python for ML in the next release > > > > чт, 5 мар. 2020 г., 17:05 AG <ag...@protonmail.com.invalid>: > > > > > > > > Thanks, for the reply! > > > > > > It looks like a high-level API similar to Sklearn pipelines. > > > In my opinion, for the first steps easier to add simple assess to gain > > the > > > ability to run a simple model or simple preprocessor from python. > > > > > > According to your example: > > > Here is raw dataset, already inside this cluster cache "myName", with > > > Label column "MyLable". > > > > > > I want to run from notebook UI imputer and knn using python API. Export > > > results to file storage as an example. > > > > > > In my opinion, the ability to create such a simple workflow should be > our > > > goal for the first time. > > > > > > Thank You! > > > > > > Best regards, > > > Andrei Gavrilov. > > > > > > Sent with ProtonMail Secure Email. > > > > > > ‐‐‐‐‐‐‐ Original Message ‐‐‐‐‐‐‐ > > > On Wednesday, March 4, 2020 10:49 PM, kencottrell < > > > ken.cottr...@gridgain.com> wrote: > > > > > > > Andrei, > > > > > > > > I am also working with Apache Ignite ML and am interested in > providing > > > > wrappers for Ignite ML API, but am wondering if instead of simply > > > recreating > > > > the low level Java API for ML inside Python, how about creating some > > > higher > > > > level services "Auto ML" workflow ? For example: > > > > > > > > 1. here is raw dataset, already inside this cluster cache "myName", > > with > > > > Label column "MyLable" , take N samples tell me which appear to > be > > > numeric, > > > > unique id, and categorical values? > > > > > > > > 2. based on N samples, , please run some analysis and tell me the > top > > 5 > > > > feature columns in terms of predictive value using algorithm = > > > RandonForest > > > > > > > > 3. do a batch run, sample size = N, using these preprocessing steps > > list > > > > {impute, scale, etc} and algorithms (knn, Decision Tree, etc} and > > > give me a > > > > report of accuracies obtain with each. > > > > > > > > In other words, we have a simple sample in the Tutorial demo > where > > > these > > > > all run and then we compare outputs - why not automate these > with a > > > Python > > > > Notebook UI of some sort? > > > > > > > > -- > > > > Sent from: > http://apache-ignite-developers.2346864.n4.nabble.com/ > > > > > > > > > > > > > > > >
