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/
> > >
> >
> >
> >
>
