[ENH] Simplified Publish API with Automatic Type Recognition by Omswastik-11 · Pull Request #1554 · openml/openml-python

Omswastik-11 · 2025-12-24T10:27:02Z

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)

examples/Basics/simple_flows_and_runs_tutorial.py

openml/__init__.py

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 · 2025-12-25T08:07:12Z

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 · 2025-12-31T10:11:19Z

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 · 2025-12-31T13:11:48Z

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 · 2026-01-06T12:44:04Z

Codecov Report

❌ Patch coverage is 36.84211% with 12 lines in your changes missing coverage. Please review.
✅ Project coverage is 52.63%. Comparing base (25ba6f8) to head (605daad).

Files with missing lines	Patch %	Lines
openml/publishing.py	33.33%	12 Missing ⚠️

Additional details and impacted files

@@            Coverage Diff             @@
##             main    #1554      +/-   ##
==========================================
- Coverage   52.70%   52.63%   -0.07%     
==========================================
  Files          37       38       +1     
  Lines        4385     4404      +19     
==========================================
+ Hits         2311     2318       +7     
- Misses       2074     2086      +12

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openml/__init__.py

jgyasu · 2026-01-13T08:57:36Z

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.

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openml/publish.py

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tests/test_openml/test_openml.py

openml/__init__.py

openml/publish.py

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openml/publish.py

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tests/test_openml/test_openml.py

openml/publishing.py

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openml/init.py:33

openml.publish(...) is used by the new tests/examples, but the package top-level does not expose a publish attribute (only the publishing submodule is imported). This will raise AttributeError for openml.publish(...). Re-export the function from openml.publishing (and add it to __all__), or update the call sites to use openml.publishing.publish(...) consistently.

from . import (
    _api_calls,
    config,
    datasets,
    evaluations,
    exceptions,
    extensions,
    flows,
    publishing,
    runs,
    setups,
    study,
    tasks,

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openml/publishing.py

tests/test_openml/test_openml.py

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Copilot · 2026-03-11T09:34:10Z

examples/Basics/simple_flows_and_runs_tutorial.py

 # ## Upload the machine learning experiments to OpenML
-# First, create a fow and fill it with metadata about the machine learning model.
+#
+# ### Option A: Automatic publishing (simplified)
+# The publish function automatically detects the model type and creates the flow:

 # %%
-knn_flow = openml.flows.OpenMLFlow(
-    # Metadata
-    model=clf,  # or None, if you do not want to upload the model object.
-    name="CustomKNeighborsClassifier",
-    description="A custom KNeighborsClassifier flow for OpenML.",
-    external_version=f"{sklearn.__version__}",
-    language="English",
-    tags=["openml_tutorial_knn"],
-    dependencies=f"{sklearn.__version__}",
-    # Hyperparameters
-    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"}
-    },
-    # If you have a pipeline with subcomponents, such as preprocessing, add them here.
-    components={},
-)
-knn_flow.publish()
-print(f"knn_flow was published with the ID {knn_flow.flow_id}")
+knn_flow = openml.publish(clf, tags=["openml_tutorial_knn"])
+print(f"Flow was auto-published with ID {knn_flow.flow_id}")


This tutorial now uses openml.publish(clf, ...) which requires an installed/registered scikit-learn extension (typically openml-sklearn). Since the script doesn’t import openml_sklearn or mention the dependency, users running the example without that extra will get a ValueError. Consider adding a short note (or an explicit import openml_sklearn # noqa: F401) near the top so the example is self-contained and the requirement is clear.

Copilot · 2026-03-11T09:34:10Z

openml/publishing.py

+        if tags and hasattr(obj, "tags"):
+            existing = obj.tags or []
+            if all(isinstance(tag, str) for tag in existing):
+                obj.tags = list(dict.fromkeys([*existing, *tags]))
+        if name is not None and hasattr(obj, "name"):
+            obj.name = name
+        return obj.publish()


tags is typed as Sequence[str], but at runtime passing a single string (e.g., tags="foo") will be treated as an iterable of characters and will silently add "f", "o", "o". It would be safer to validate that tags is not a str (and that all provided tags are strings) and raise a clear TypeError/ValueError when the input is invalid.

improve publish api for users

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Omswastik-11 changed the title ~~[ENH] improve publish api for users~~ [ENH] Simplified Publish API with Automatic Type Recognition Dec 24, 2025

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Omswastik-11 added 3 commits December 25, 2025 13:20

improve doc-string

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Merge branch 'main' into prototype-publish

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Merge branch 'main' into prototype-publish

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Merge branch 'main' into prototype-publish

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move publish func to a separate file

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Merge branch 'main' into prototype-publish

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Merge branch 'main' into prototype-publish

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