[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

[codecov-commenter]

Codecov Report

❌ Patch coverage is 28.00000% with 18 lines in your changes missing coverage. Please review.
✅ Project coverage is 51.92%. Comparing base (d421b9e) to head (76a4d3f).

Files with missing lines	Patch %	Lines
openml/publish.py	25.00%	18 Missing ⚠️

Additional details and impacted files

@@            Coverage Diff             @@
##             main    #1554      +/-   ##
==========================================
- Coverage   52.04%   51.92%   -0.12%     
==========================================
  Files          36       37       +1     
  Lines        4333     4358      +25     
==========================================
+ Hits         2255     2263       +8     
- Misses       2078     2095      +17     

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[jgyasu]

I have added some comments. I also feel we should not populate __init__.py with these functions, we can have them in a seperate file and use __init__.py only for imports.