[ENH] V1 → V2 API Migration - studies

[rohansen856]

Metadata

• Reference Issue: [ENH] V1 → V2 API Migration - studies #1594 (towards [ENH] V1 → V2 API Migration #1575)
• New Tests Added: No
• Documentation Updated: No

Details

Stackend PR, Depends on #1576

This PR adds Studies v2 migration.

A question:
Due to the pre commit hook i could not put 6 arguments in a function, so i had to workaround that with this instead:
openml_api\resources\studies.py (line 10-15)

        limit = kwargs.get("limit")
        offset = kwargs.get("offset")
        status = kwargs.get("status")
        main_entity_type = kwargs.get("main_entity_type")
        uploader = kwargs.get("uploader")
        benchmark_suite = kwargs.get("benchmark_suite")

I would like to confirm if this approach is correct or not. Raising a draft PR for now.

[codecov-commenter]

Codecov Report

❌ Patch coverage is 75.00000% with 65 lines in your changes missing coverage. Please review.
✅ Project coverage is 54.26%. Comparing base (645ef01) to head (9170edc).

Files with missing lines	Patch %	Lines
openml/_api/resources/studies.py	21.05%	30 Missing ⚠️
openml/_api/http/client.py	82.60%	12 Missing ⚠️
openml/_api/resources/tasks.py	87.23%	6 Missing ⚠️
openml/_api/runtime/fallback.py	0.00%	6 Missing ⚠️
openml/_api/runtime/core.py	82.14%	5 Missing ⚠️
openml/_api/resources/datasets.py	77.77%	2 Missing ⚠️
openml/_api/__init__.py	75.00%	1 Missing ⚠️
openml/_api/config.py	96.87%	1 Missing ⚠️
openml/study/functions.py	75.00%	1 Missing ⚠️
openml/tasks/functions.py	87.50%	1 Missing ⚠️

Additional details and impacted files

@@            Coverage Diff             @@
##             main    #1610      +/-   ##
==========================================
+ Coverage   52.78%   54.26%   +1.48%     
==========================================
  Files          36       47      +11     
  Lines        4331     4559     +228     
==========================================
+ Hits         2286     2474     +188     
- Misses       2045     2085      +40     

☔ View full report in Codecov by Sentry.
📢 Have feedback on the report? Share it here.

🚀 New features to boost your workflow:

• ❄️ Test Analytics: Detect flaky tests, report on failures, and find test suite problems.

[rohansen856]

Implementing noqa instead of the kwargs following example from here: openml\testing.py:

    def _check_fold_timing_evaluations(  # noqa: PLR0913
        self,
        fold_evaluations: dict[str, dict[int, dict[int, float]]],
        num_repeats: int,
        num_folds: int,
        *,
        max_time_allowed: float = 60000.0,
        task_type: TaskType = TaskType.SUPERVISED_CLASSIFICATION,
        check_scores: bool = True,
    ) -> None:

Final function signature:

    def list(  # noqa: PLR0913
        self,
        limit: int | None = None,
        offset: int | None = None,
        status: str | None = None,
        main_entity_type: str | None = None,
        uploader: list[int] | None = None,
        benchmark_suite: int | None = None,
    ) -> Any:

[geetu040]

Good work. Just use the listing as suggested in #1575 (comment) which is already similar to what you have done.

[rohansen856]

@geetu040 I reviewed the specific changes needed and have a slight doubt in the pandas implementation.
So as i undertand, i need to use pandas Dataframe insteaf of ANY in openml\_api\resources\base.py like this:

class StudiesAPI(ResourceAPI, ABC):
    @abstractmethod
    def list(  # noqa: PLR0913
        self,
        limit: int | None = None,
        offset: int | None = None,
        status: str | None = None,
        main_entity_type: str | None = None,
        uploader: list[int] | None = None,
        benchmark_suite: int | None = None,
    ) -> pd.DataFrame: ...

and similarly i have to change the return object in openml\_api\resources\studies.py from this:return response.text
to this:

xml_string = response.text

        # Parse XML and convert to DataFrame
        study_dict = xmltodict.parse(xml_string, force_list=("oml:study",))

        # Minimalistic check if the XML is useful
        assert isinstance(study_dict["oml:study_list"]["oml:study"], list), type(
            study_dict["oml:study_list"],
        )
        assert (
            study_dict["oml:study_list"]["@xmlns:oml"] == "http://openml.org/openml"
        ), study_dict["oml:study_list"]["@xmlns:oml"]

        studies = {}
        for study_ in study_dict["oml:study_list"]["oml:study"]:
            # maps from xml name to a tuple of (dict name, casting fn)
            expected_fields = {
                "oml:id": ("id", int),
                "oml:alias": ("alias", str),
                "oml:main_entity_type": ("main_entity_type", str),
                "oml:benchmark_suite": ("benchmark_suite", int),
                "oml:name": ("name", str),
                "oml:status": ("status", str),
                "oml:creation_date": ("creation_date", str),
                "oml:creator": ("creator", int),
            }
            study_id = int(study_["oml:id"])
            current_study = {}
            for oml_field_name, (real_field_name, cast_fn) in expected_fields.items():
                if oml_field_name in study_:
                    current_study[real_field_name] = cast_fn(study_[oml_field_name])
            current_study["id"] = int(current_study["id"])
            studies[study_id] = current_study

        return pd.DataFrame.from_dict(studies, orient="index")

A total of 3 files would be affected: openml\_api\resources\base.py, openml\_api\resources\studies.py and openml\study\functions.py

Can you please confirm my approach... After that i will update the PR.

[geetu040]

@rohansen856 yes sounds right

[rohansen856]

Updated! Ready for review.