[ENH] Simplified Publish API with Automatic Type Recognition

[Omswastik-11]

initially

from openml_sklearn.extension import SklearnExtension
from sklearn.neighbors import KNeighborsClassifier
clf = KNeighborsClassifier(n_neighbors=3)
extension = SklearnExtension()# User instantiates the extension object
knn_flow = extension.model_to_flow(clf) # User manually converts the model (estimator instance) to an OpenMLFlow object
knn_flow.publish()

API

from sklearn.neighbors import KNeighborsClassifier
import openml_sklearn  # Register the extension
import openml

clf = KNeighborsClassifier(n_neighbors=3)

openml.publish(clf)

[fkiraly]

I get this is a draft still, some early comments.

• works for flows only, I would recommend to try for at least two different object types to see the dispatching challenge there.
• do the extension checking inside publish and not in the usage example

[Omswastik-11]

Thanks @fkiraly !!
I checked on flow , datset , task . it is working correctly but in run it is getting some server side issues.

Task 1 failed: https://test.openml.org/api/v1/xml/data/features/1 returned code 274: No features found. Additionally, dataset processed with error - None

[jgyasu]

The PR description is not entirely correct. This is how the interface looks currently:

from openml_sklearn.extension import SklearnExtension
from sklearn.neighbors import KNeighborsClassifier
clf = KNeighborsClassifier(n_neighbors=3)
extension = SklearnExtension()# User instantiates the extension object
knn_flow = extension.model_to_flow(clf) # User manually converts the model (estimator instance) to an OpenMLFlow object
knn_flow.publish()

But I like the idea of a unified publish. I am currently working on a design document for refactoring Extension and this design coincides mine as well, which is a good thing.

[Omswastik-11]

The PR description is not entirely correct. This is how the interface looks currently:

from openml_sklearn.extension import SklearnExtension
from sklearn.neighbors import KNeighborsClassifier
clf = KNeighborsClassifier(n_neighbors=3)
extension = SklearnExtension()# User instantiates the extension object
knn_flow = extension.model_to_flow(clf) # User manually converts the model (estimator instance) to an OpenMLFlow object
knn_flow.publish()

But I like the idea of a unified publish. I am currently working on a design document for refactoring Extension and this design coincides mine as well, which is a good thing.

Thanks for the correction I used the syntax example used in example script . this unified publish was Franz's idea . https://github.com/gc-os-ai/openml-project-dev/issues/8