[ENH] Simplified Publish API with Automatic Type Recognition

[Omswastik-11]

initially

import sklearn
import openml

clf = KNeighborsClassifier(n_neighbors=3)
clf.fit(X_train, y_train)

# Verbose manual flow construction required
knn_flow = openml.flows.OpenMLFlow(
    model=clf,
    name="CustomKNeighborsClassifier",
    description="A custom KNeighborsClassifier flow for OpenML.",
    external_version=f"{sklearn.__version__}",
    language="English",
    tags=["openml_tutorial_knn"],
    dependencies=f"{sklearn.__version__}",
    parameters={k: str(v) for k, v in knn_parameters.items()},
    parameters_meta_info={
        "n_neighbors": {"description": "number of neighbors to use", "data_type": "int"}
    },
    components={},
)
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