From 0159f474c6bbc15f20d52bc946bd252bd852b196 Mon Sep 17 00:00:00 2001 From: geetu040 Date: Tue, 30 Dec 2025 09:11:27 +0500 Subject: [PATCH 01/10] set up folder structure and base code --- openml/_api/__init__.py | 8 +++ openml/_api/config.py | 5 ++ openml/_api/http/__init__.py | 1 + openml/_api/http/client.py | 23 ++++++ openml/_api/http/utils.py | 0 openml/_api/resources/__init__.py | 2 + openml/_api/resources/base.py | 22 ++++++ openml/_api/resources/datasets.py | 13 ++++ openml/_api/resources/tasks.py | 113 ++++++++++++++++++++++++++++++ openml/_api/runtime/core.py | 58 +++++++++++++++ openml/_api/runtime/fallback.py | 5 ++ openml/tasks/functions.py | 8 ++- 12 files changed, 255 insertions(+), 3 deletions(-) create mode 100644 openml/_api/__init__.py create mode 100644 openml/_api/config.py create mode 100644 openml/_api/http/__init__.py create mode 100644 openml/_api/http/client.py create mode 100644 openml/_api/http/utils.py create mode 100644 openml/_api/resources/__init__.py create mode 100644 openml/_api/resources/base.py create mode 100644 openml/_api/resources/datasets.py create mode 100644 openml/_api/resources/tasks.py create mode 100644 openml/_api/runtime/core.py create mode 100644 openml/_api/runtime/fallback.py diff --git a/openml/_api/__init__.py b/openml/_api/__init__.py new file mode 100644 index 000000000..5089f94dd --- /dev/null +++ b/openml/_api/__init__.py @@ -0,0 +1,8 @@ +from openml._api.runtime.core import APIContext + + +def set_api_version(version: str, strict=False): + api_context.set_version(version=version, strict=strict) + + +api_context = APIContext() diff --git a/openml/_api/config.py b/openml/_api/config.py new file mode 100644 index 000000000..bd93c3cad --- /dev/null +++ b/openml/_api/config.py @@ -0,0 +1,5 @@ +from __future__ import annotations + +API_V1_SERVER = "https://www.openml.org/api/v1/xml" +API_V2_SERVER = "http://127.0.0.1:8001" +API_KEY = "..." diff --git a/openml/_api/http/__init__.py b/openml/_api/http/__init__.py new file mode 100644 index 000000000..fde2a5b0a --- /dev/null +++ b/openml/_api/http/__init__.py @@ -0,0 +1 @@ +from openml._api.http.client import HTTPClient diff --git a/openml/_api/http/client.py b/openml/_api/http/client.py new file mode 100644 index 000000000..81a9213e3 --- /dev/null +++ b/openml/_api/http/client.py @@ -0,0 +1,23 @@ +from __future__ import annotations + +import requests + +from openml.__version__ import __version__ + + +class HTTPClient: + def __init__(self, base_url: str): + self.base_url = base_url + self.headers = {"user-agent": f"openml-python/{__version__}"} + + def get(self, path, params=None): + url = f"{self.base_url}/{path}" + return requests.get(url, params=params, headers=self.headers) + + def post(self, path, data=None, files=None): + url = f"{self.base_url}/{path}" + return requests.post(url, data=data, files=files, headers=self.headers) + + def delete(self, path, params=None): + url = f"{self.base_url}/{path}" + return requests.delete(url, params=params, headers=self.headers) diff --git a/openml/_api/http/utils.py b/openml/_api/http/utils.py new file mode 100644 index 000000000..e69de29bb diff --git a/openml/_api/resources/__init__.py b/openml/_api/resources/__init__.py new file mode 100644 index 000000000..078fc5998 --- /dev/null +++ b/openml/_api/resources/__init__.py @@ -0,0 +1,2 @@ +from openml._api.resources.datasets import DatasetsV1, DatasetsV2 +from openml._api.resources.tasks import TasksV1, TasksV2 diff --git a/openml/_api/resources/base.py b/openml/_api/resources/base.py new file mode 100644 index 000000000..1fae27665 --- /dev/null +++ b/openml/_api/resources/base.py @@ -0,0 +1,22 @@ +from __future__ import annotations + +from abc import ABC, abstractmethod +from typing import TYPE_CHECKING + +if TYPE_CHECKING: + from openml._api.http import HTTPClient + + +class ResourceAPI: + def __init__(self, http: HTTPClient): + self._http = http + + +class DatasetsAPI(ResourceAPI, ABC): + @abstractmethod + def get(self, id: int) -> dict: ... + + +class TasksAPI(ResourceAPI, ABC): + @abstractmethod + def get(self, id: int) -> dict: ... diff --git a/openml/_api/resources/datasets.py b/openml/_api/resources/datasets.py new file mode 100644 index 000000000..cd1bb595a --- /dev/null +++ b/openml/_api/resources/datasets.py @@ -0,0 +1,13 @@ +from __future__ import annotations + +from openml._api.resources.base import DatasetsAPI + + +class DatasetsV1(DatasetsAPI): + def get(self, id): + pass + + +class DatasetsV2(DatasetsAPI): + def get(self, id): + pass diff --git a/openml/_api/resources/tasks.py b/openml/_api/resources/tasks.py new file mode 100644 index 000000000..b0e9afbf8 --- /dev/null +++ b/openml/_api/resources/tasks.py @@ -0,0 +1,113 @@ +from __future__ import annotations + +import xmltodict + +from openml._api.resources.base import TasksAPI +from openml.tasks.task import ( + OpenMLClassificationTask, + OpenMLClusteringTask, + OpenMLLearningCurveTask, + OpenMLRegressionTask, + OpenMLTask, + TaskType, +) + + +class TasksV1(TasksAPI): + def get(self, id, return_response=False): + path = f"task/{id}" + response = self._http.get(path) + xml_content = response.content + task = self._create_task_from_xml(xml_content) + + if return_response: + return task, response + + return task + + def _create_task_from_xml(self, xml: str) -> OpenMLTask: + """Create a task given a xml string. + + Parameters + ---------- + xml : string + Task xml representation. + + Returns + ------- + OpenMLTask + """ + dic = xmltodict.parse(xml)["oml:task"] + estimation_parameters = {} + inputs = {} + # Due to the unordered structure we obtain, we first have to extract + # the possible keys of oml:input; dic["oml:input"] is a list of + # OrderedDicts + + # Check if there is a list of inputs + if isinstance(dic["oml:input"], list): + for input_ in dic["oml:input"]: + name = input_["@name"] + inputs[name] = input_ + # Single input case + elif isinstance(dic["oml:input"], dict): + name = dic["oml:input"]["@name"] + inputs[name] = dic["oml:input"] + + evaluation_measures = None + if "evaluation_measures" in inputs: + evaluation_measures = inputs["evaluation_measures"]["oml:evaluation_measures"][ + "oml:evaluation_measure" + ] + + task_type = TaskType(int(dic["oml:task_type_id"])) + common_kwargs = { + "task_id": dic["oml:task_id"], + "task_type": dic["oml:task_type"], + "task_type_id": task_type, + "data_set_id": inputs["source_data"]["oml:data_set"]["oml:data_set_id"], + "evaluation_measure": evaluation_measures, + } + # TODO: add OpenMLClusteringTask? + if task_type in ( + TaskType.SUPERVISED_CLASSIFICATION, + TaskType.SUPERVISED_REGRESSION, + TaskType.LEARNING_CURVE, + ): + # Convert some more parameters + for parameter in inputs["estimation_procedure"]["oml:estimation_procedure"][ + "oml:parameter" + ]: + name = parameter["@name"] + text = parameter.get("#text", "") + estimation_parameters[name] = text + + common_kwargs["estimation_procedure_type"] = inputs["estimation_procedure"][ + "oml:estimation_procedure" + ]["oml:type"] + common_kwargs["estimation_procedure_id"] = int( + inputs["estimation_procedure"]["oml:estimation_procedure"]["oml:id"] + ) + + common_kwargs["estimation_parameters"] = estimation_parameters + common_kwargs["target_name"] = inputs["source_data"]["oml:data_set"][ + "oml:target_feature" + ] + common_kwargs["data_splits_url"] = inputs["estimation_procedure"][ + "oml:estimation_procedure" + ]["oml:data_splits_url"] + + cls = { + TaskType.SUPERVISED_CLASSIFICATION: OpenMLClassificationTask, + TaskType.SUPERVISED_REGRESSION: OpenMLRegressionTask, + TaskType.CLUSTERING: OpenMLClusteringTask, + TaskType.LEARNING_CURVE: OpenMLLearningCurveTask, + }.get(task_type) + if cls is None: + raise NotImplementedError(f"Task type {common_kwargs['task_type']} not supported.") + return cls(**common_kwargs) # type: ignore + + +class TasksV2(TasksAPI): + def get(self, id): + pass diff --git a/openml/_api/runtime/core.py b/openml/_api/runtime/core.py new file mode 100644 index 000000000..80f35587c --- /dev/null +++ b/openml/_api/runtime/core.py @@ -0,0 +1,58 @@ +from __future__ import annotations + +from openml._api.config import ( + API_V1_SERVER, + API_V2_SERVER, +) +from openml._api.http.client import HTTPClient +from openml._api.resources import ( + DatasetsV1, + DatasetsV2, + TasksV1, + TasksV2, +) +from openml._api.runtime.fallback import FallbackProxy + + +class APIBackend: + def __init__(self, *, datasets, tasks): + self.datasets = datasets + self.tasks = tasks + + +def build_backend(version: str, strict: bool) -> APIBackend: + v1_http = HTTPClient(API_V1_SERVER) + v2_http = HTTPClient(API_V2_SERVER) + + v1 = APIBackend( + datasets=DatasetsV1(v1_http), + tasks=TasksV1(v1_http), + ) + + if version == "v1": + return v1 + + v2 = APIBackend( + datasets=DatasetsV2(v2_http), + tasks=TasksV2(v2_http), + ) + + if strict: + return v2 + + return APIBackend( + datasets=FallbackProxy(v2.datasets, v1.datasets), + tasks=FallbackProxy(v2.tasks, v1.tasks), + ) + + +class APIContext: + def __init__(self): + self._backend = build_backend("v1", strict=False) + + def set_version(self, version: str, strict: bool = False): + self._backend = build_backend(version, strict) + + @property + def backend(self): + return self._backend diff --git a/openml/_api/runtime/fallback.py b/openml/_api/runtime/fallback.py new file mode 100644 index 000000000..56e96a966 --- /dev/null +++ b/openml/_api/runtime/fallback.py @@ -0,0 +1,5 @@ +from __future__ import annotations + + +class FallbackProxy: + pass diff --git a/openml/tasks/functions.py b/openml/tasks/functions.py index d2bf5e946..91be65965 100644 --- a/openml/tasks/functions.py +++ b/openml/tasks/functions.py @@ -12,6 +12,7 @@ import openml._api_calls import openml.utils +from openml._api import api_context from openml.datasets import get_dataset from openml.exceptions import OpenMLCacheException @@ -442,11 +443,12 @@ def _get_task_description(task_id: int) -> OpenMLTask: except OpenMLCacheException: _cache_dir = openml.utils._create_cache_directory_for_id(TASKS_CACHE_DIR_NAME, task_id) xml_file = _cache_dir / "task.xml" - task_xml = openml._api_calls._perform_api_call("task/%d" % task_id, "get") + task, response = api_context.backend.tasks.get(task_id, return_response=True) with xml_file.open("w", encoding="utf8") as fh: - fh.write(task_xml) - return _create_task_from_xml(task_xml) + fh.write(response.text) + + return task def _create_task_from_xml(xml: str) -> OpenMLTask: From 52ef37999fad8509e5e85b8512e442bd9dc69e04 Mon Sep 17 00:00:00 2001 From: geetu040 Date: Mon, 5 Jan 2026 12:48:58 +0500 Subject: [PATCH 02/10] fix pre-commit --- openml/_api/__init__.py | 2 +- openml/_api/http/__init__.py | 2 ++ openml/_api/http/client.py | 32 +++++++++++++++++++++++-------- openml/_api/resources/__init__.py | 2 ++ openml/_api/resources/base.py | 13 +++++++++++-- openml/_api/resources/datasets.py | 15 +++++++++++---- openml/_api/resources/tasks.py | 25 +++++++++++++++++++----- openml/_api/runtime/__init__.py | 0 openml/_api/runtime/core.py | 23 +++++++++++----------- openml/_api/runtime/fallback.py | 9 ++++++++- openml/tasks/functions.py | 12 ++++++++---- 11 files changed, 99 insertions(+), 36 deletions(-) create mode 100644 openml/_api/runtime/__init__.py diff --git a/openml/_api/__init__.py b/openml/_api/__init__.py index 5089f94dd..881f40671 100644 --- a/openml/_api/__init__.py +++ b/openml/_api/__init__.py @@ -1,7 +1,7 @@ from openml._api.runtime.core import APIContext -def set_api_version(version: str, strict=False): +def set_api_version(version: str, *, strict: bool = False) -> None: api_context.set_version(version=version, strict=strict) diff --git a/openml/_api/http/__init__.py b/openml/_api/http/__init__.py index fde2a5b0a..8e6d1e4ce 100644 --- a/openml/_api/http/__init__.py +++ b/openml/_api/http/__init__.py @@ -1 +1,3 @@ from openml._api.http.client import HTTPClient + +__all__ = ["HTTPClient"] diff --git a/openml/_api/http/client.py b/openml/_api/http/client.py index 81a9213e3..dea5de809 100644 --- a/openml/_api/http/client.py +++ b/openml/_api/http/client.py @@ -1,23 +1,39 @@ from __future__ import annotations +from typing import Any, Mapping + import requests +from requests import Response from openml.__version__ import __version__ class HTTPClient: - def __init__(self, base_url: str): + def __init__(self, base_url: str) -> None: self.base_url = base_url - self.headers = {"user-agent": f"openml-python/{__version__}"} + self.headers: dict[str, str] = {"user-agent": f"openml-python/{__version__}"} - def get(self, path, params=None): + def get( + self, + path: str, + params: Mapping[str, Any] | None = None, + ) -> Response: url = f"{self.base_url}/{path}" - return requests.get(url, params=params, headers=self.headers) + return requests.get(url, params=params, headers=self.headers, timeout=10) - def post(self, path, data=None, files=None): + def post( + self, + path: str, + data: Mapping[str, Any] | None = None, + files: Any = None, + ) -> Response: url = f"{self.base_url}/{path}" - return requests.post(url, data=data, files=files, headers=self.headers) + return requests.post(url, data=data, files=files, headers=self.headers, timeout=10) - def delete(self, path, params=None): + def delete( + self, + path: str, + params: Mapping[str, Any] | None = None, + ) -> Response: url = f"{self.base_url}/{path}" - return requests.delete(url, params=params, headers=self.headers) + return requests.delete(url, params=params, headers=self.headers, timeout=10) diff --git a/openml/_api/resources/__init__.py b/openml/_api/resources/__init__.py index 078fc5998..b1af3c1a8 100644 --- a/openml/_api/resources/__init__.py +++ b/openml/_api/resources/__init__.py @@ -1,2 +1,4 @@ from openml._api.resources.datasets import DatasetsV1, DatasetsV2 from openml._api.resources.tasks import TasksV1, TasksV2 + +__all__ = ["DatasetsV1", "DatasetsV2", "TasksV1", "TasksV2"] diff --git a/openml/_api/resources/base.py b/openml/_api/resources/base.py index 1fae27665..6fbf8977d 100644 --- a/openml/_api/resources/base.py +++ b/openml/_api/resources/base.py @@ -4,7 +4,11 @@ from typing import TYPE_CHECKING if TYPE_CHECKING: + from requests import Response + from openml._api.http import HTTPClient + from openml.datasets.dataset import OpenMLDataset + from openml.tasks.task import OpenMLTask class ResourceAPI: @@ -14,9 +18,14 @@ def __init__(self, http: HTTPClient): class DatasetsAPI(ResourceAPI, ABC): @abstractmethod - def get(self, id: int) -> dict: ... + def get(self, dataset_id: int) -> OpenMLDataset | tuple[OpenMLDataset, Response]: ... class TasksAPI(ResourceAPI, ABC): @abstractmethod - def get(self, id: int) -> dict: ... + def get( + self, + task_id: int, + *, + return_response: bool = False, + ) -> OpenMLTask | tuple[OpenMLTask, Response]: ... diff --git a/openml/_api/resources/datasets.py b/openml/_api/resources/datasets.py index cd1bb595a..9ff1ec278 100644 --- a/openml/_api/resources/datasets.py +++ b/openml/_api/resources/datasets.py @@ -1,13 +1,20 @@ from __future__ import annotations +from typing import TYPE_CHECKING + from openml._api.resources.base import DatasetsAPI +if TYPE_CHECKING: + from responses import Response + + from openml.datasets.dataset import OpenMLDataset + class DatasetsV1(DatasetsAPI): - def get(self, id): - pass + def get(self, dataset_id: int) -> OpenMLDataset | tuple[OpenMLDataset, Response]: + raise NotImplementedError class DatasetsV2(DatasetsAPI): - def get(self, id): - pass + def get(self, dataset_id: int) -> OpenMLDataset | tuple[OpenMLDataset, Response]: + raise NotImplementedError diff --git a/openml/_api/resources/tasks.py b/openml/_api/resources/tasks.py index b0e9afbf8..f494fb9a3 100644 --- a/openml/_api/resources/tasks.py +++ b/openml/_api/resources/tasks.py @@ -1,5 +1,7 @@ from __future__ import annotations +from typing import TYPE_CHECKING + import xmltodict from openml._api.resources.base import TasksAPI @@ -12,12 +14,20 @@ TaskType, ) +if TYPE_CHECKING: + from requests import Response + class TasksV1(TasksAPI): - def get(self, id, return_response=False): - path = f"task/{id}" + def get( + self, + task_id: int, + *, + return_response: bool = False, + ) -> OpenMLTask | tuple[OpenMLTask, Response]: + path = f"task/{task_id}" response = self._http.get(path) - xml_content = response.content + xml_content = response.text task = self._create_task_from_xml(xml_content) if return_response: @@ -109,5 +119,10 @@ def _create_task_from_xml(self, xml: str) -> OpenMLTask: class TasksV2(TasksAPI): - def get(self, id): - pass + def get( + self, + task_id: int, + *, + return_response: bool = False, + ) -> OpenMLTask | tuple[OpenMLTask, Response]: + raise NotImplementedError diff --git a/openml/_api/runtime/__init__.py b/openml/_api/runtime/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/openml/_api/runtime/core.py b/openml/_api/runtime/core.py index 80f35587c..aa09a69db 100644 --- a/openml/_api/runtime/core.py +++ b/openml/_api/runtime/core.py @@ -1,5 +1,7 @@ from __future__ import annotations +from typing import TYPE_CHECKING + from openml._api.config import ( API_V1_SERVER, API_V2_SERVER, @@ -11,16 +13,18 @@ TasksV1, TasksV2, ) -from openml._api.runtime.fallback import FallbackProxy + +if TYPE_CHECKING: + from openml._api.resources.base import DatasetsAPI, TasksAPI class APIBackend: - def __init__(self, *, datasets, tasks): + def __init__(self, *, datasets: DatasetsAPI, tasks: TasksAPI): self.datasets = datasets self.tasks = tasks -def build_backend(version: str, strict: bool) -> APIBackend: +def build_backend(version: str, *, strict: bool) -> APIBackend: v1_http = HTTPClient(API_V1_SERVER) v2_http = HTTPClient(API_V2_SERVER) @@ -40,19 +44,16 @@ def build_backend(version: str, strict: bool) -> APIBackend: if strict: return v2 - return APIBackend( - datasets=FallbackProxy(v2.datasets, v1.datasets), - tasks=FallbackProxy(v2.tasks, v1.tasks), - ) + return v1 class APIContext: - def __init__(self): + def __init__(self) -> None: self._backend = build_backend("v1", strict=False) - def set_version(self, version: str, strict: bool = False): - self._backend = build_backend(version, strict) + def set_version(self, version: str, *, strict: bool = False) -> None: + self._backend = build_backend(version=version, strict=strict) @property - def backend(self): + def backend(self) -> APIBackend: return self._backend diff --git a/openml/_api/runtime/fallback.py b/openml/_api/runtime/fallback.py index 56e96a966..1bc99d270 100644 --- a/openml/_api/runtime/fallback.py +++ b/openml/_api/runtime/fallback.py @@ -1,5 +1,12 @@ from __future__ import annotations +from typing import TYPE_CHECKING + +if TYPE_CHECKING: + from openml._api.resources.base import ResourceAPI + class FallbackProxy: - pass + def __init__(self, primary: ResourceAPI, fallback: ResourceAPI): + self._primary = primary + self._fallback = fallback diff --git a/openml/tasks/functions.py b/openml/tasks/functions.py index ef67f75bf..a794ad56d 100644 --- a/openml/tasks/functions.py +++ b/openml/tasks/functions.py @@ -445,10 +445,14 @@ def _get_task_description(task_id: int) -> OpenMLTask: except OpenMLCacheException: _cache_dir = openml.utils._create_cache_directory_for_id(TASKS_CACHE_DIR_NAME, task_id) xml_file = _cache_dir / "task.xml" - task, response = api_context.backend.tasks.get(task_id, return_response=True) - - with xml_file.open("w", encoding="utf8") as fh: - fh.write(response.text) + result = api_context.backend.tasks.get(task_id, return_response=True) + + if isinstance(result, tuple): + task, response = result + with xml_file.open("w", encoding="utf8") as fh: + fh.write(response.text) + else: + task = result return task From f7ba710a9a3c457ec7c48ec45fa174c9194eeb98 Mon Sep 17 00:00:00 2001 From: JATAYU000 Date: Tue, 6 Jan 2026 16:24:35 +0530 Subject: [PATCH 03/10] Merge base migration pr, ruff --- openml/_api/http/client.py | 5 +- openml/_api/resources/base.py | 70 ++++- openml/_api/resources/datasets.py | 440 +++++++++++++++++++++++++++++- 3 files changed, 504 insertions(+), 11 deletions(-) diff --git a/openml/_api/http/client.py b/openml/_api/http/client.py index dea5de809..b0d3c911f 100644 --- a/openml/_api/http/client.py +++ b/openml/_api/http/client.py @@ -25,10 +25,13 @@ def post( self, path: str, data: Mapping[str, Any] | None = None, + json: dict | None = None, files: Any = None, ) -> Response: url = f"{self.base_url}/{path}" - return requests.post(url, data=data, files=files, headers=self.headers, timeout=10) + return requests.post( + url, data=data, json=json, files=files, headers=self.headers, timeout=10 + ) def delete( self, diff --git a/openml/_api/resources/base.py b/openml/_api/resources/base.py index 6fbf8977d..9d480b06a 100644 --- a/openml/_api/resources/base.py +++ b/openml/_api/resources/base.py @@ -4,6 +4,7 @@ from typing import TYPE_CHECKING if TYPE_CHECKING: + import pandas as pd from requests import Response from openml._api.http import HTTPClient @@ -18,7 +19,74 @@ def __init__(self, http: HTTPClient): class DatasetsAPI(ResourceAPI, ABC): @abstractmethod - def get(self, dataset_id: int) -> OpenMLDataset | tuple[OpenMLDataset, Response]: ... + def get( + self, dataset_id: int, *, return_response: bool + ) -> OpenMLDataset | tuple[OpenMLDataset, Response]: ... + + @abstractmethod + def list( # noqa: PLR0913 + self, + data_id: list[int] | None = None, + offset: int | None = None, + size: int | None = None, + status: str | None = None, + tag: str | None = None, + data_name: str | None = None, + data_version: int | None = None, + number_instances: int | str | None = None, + number_features: int | str | None = None, + number_classes: int | str | None = None, + number_missing_values: int | str | None = None, + ) -> pd.DataFrame: ... + + def _name_to_id( + self, + dataset_name: str, + version: int | None = None, + error_if_multiple: bool = False, # noqa: FBT001, FBT002 + ) -> int: + """Attempt to find the dataset id of the dataset with the given name. + + If multiple datasets with the name exist, and ``error_if_multiple`` is ``False``, + then return the least recent still active dataset. + + Raises an error if no dataset with the name is found. + Raises an error if a version is specified but it could not be found. + + Parameters + ---------- + dataset_name : str + The name of the dataset for which to find its id. + version : int, optional + Version to retrieve. If not specified, the oldest active version is returned. + error_if_multiple : bool (default=False) + If `False`, if multiple datasets match, return the least recent active dataset. + If `True`, if multiple datasets match, raise an error. + download_qualities : bool, optional (default=True) + If `True`, also download qualities.xml file. If False it skip the qualities.xml. + + Returns + ------- + int + The id of the dataset. + """ + status = None if version is not None else "active" + candidates = self.list( + data_name=dataset_name, + status=status, + data_version=version, + ) + if error_if_multiple and len(candidates) > 1: + msg = f"Multiple active datasets exist with name '{dataset_name}'." + raise ValueError(msg) + + if candidates.empty: + no_dataset_for_name = f"No active datasets exist with name '{dataset_name}'" + and_version = f" and version '{version}'." if version is not None else "." + raise RuntimeError(no_dataset_for_name + and_version) + + # Dataset ids are chronological so we can just sort based on ids (instead of version) + return candidates["did"].min() # type: ignore class TasksAPI(ResourceAPI, ABC): diff --git a/openml/_api/resources/datasets.py b/openml/_api/resources/datasets.py index 9ff1ec278..f985cd75a 100644 --- a/openml/_api/resources/datasets.py +++ b/openml/_api/resources/datasets.py @@ -1,20 +1,442 @@ from __future__ import annotations -from typing import TYPE_CHECKING - -from openml._api.resources.base import DatasetsAPI +from functools import partial +from typing import TYPE_CHECKING, Any if TYPE_CHECKING: - from responses import Response + from requests import Response - from openml.datasets.dataset import OpenMLDataset +import pandas as pd +import xmltodict + +import openml.utils +from openml._api.resources.base import DatasetsAPI +from openml.datasets.dataset import OpenMLDataset class DatasetsV1(DatasetsAPI): - def get(self, dataset_id: int) -> OpenMLDataset | tuple[OpenMLDataset, Response]: - raise NotImplementedError + def get( + self, dataset_id: int, *, return_response: bool = False + ) -> OpenMLDataset | tuple[OpenMLDataset, Response]: + path = f"data/{dataset_id}" + response = self._http.get(path) + xml_content = response.text # .text returns str, .content returns bytes + dataset = self._create_dataset_from_xml(xml_content) + + if return_response: + return dataset, response + + return dataset + + def list( # noqa: PLR0913 + self, + data_id: list[int] | None = None, + offset: int | None = None, + size: int | None = None, + status: str | None = None, + tag: str | None = None, + data_name: str | None = None, + data_version: int | None = None, + number_instances: int | str | None = None, + number_features: int | str | None = None, + number_classes: int | str | None = None, + number_missing_values: int | str | None = None, + ) -> pd.DataFrame: + """Return a dataframe of all dataset which are on OpenML. + + Supports large amount of results. + + Parameters + ---------- + data_id : list, optional + A list of data ids, to specify which datasets should be + listed + offset : int, optional + The number of datasets to skip, starting from the first. + size : int, optional + The maximum number of datasets to show. + status : str, optional + Should be {active, in_preparation, deactivated}. By + default active datasets are returned, but also datasets + from another status can be requested. + tag : str, optional + data_name : str, optional + data_version : int, optional + number_instances : int | str, optional + number_features : int | str, optional + number_classes : int | str, optional + number_missing_values : int | str, optional + + Returns + ------- + datasets: dataframe + Each row maps to a dataset + Each column contains the following information: + - dataset id + - name + - format + - status + If qualities are calculated for the dataset, some of + these are also included as columns. + """ + listing_call = partial( + self._list_datasets, + data_id=data_id, + status=status, + tag=tag, + data_name=data_name, + data_version=data_version, + number_instances=number_instances, + number_features=number_features, + number_classes=number_classes, + number_missing_values=number_missing_values, + ) + batches = openml.utils._list_all(listing_call, offset=offset, limit=size) + if len(batches) == 0: + return pd.DataFrame() + + return pd.concat(batches) + + def _list_datasets( + self, + limit: int, + offset: int, + *, + data_id: list[int] | None = None, # type: ignore + **kwargs: Any, + ) -> pd.DataFrame: + """ + Perform api call to return a list of all datasets. + + Parameters + ---------- + The arguments that are lists are separated from the single value + ones which are put into the kwargs. + display_errors is also separated from the kwargs since it has a + default value. + + limit : int + The maximum number of datasets to show. + offset : int + The number of datasets to skip, starting from the first. + data_id : list, optional + + kwargs : dict, optional + Legal filter operators (keys in the dict): + tag, status, limit, offset, data_name, data_version, number_instances, + number_features, number_classes, number_missing_values. + + Returns + ------- + datasets : dataframe + """ + api_call = "data/list" + + if limit is not None: + api_call += f"/limit/{limit}" + if offset is not None: + api_call += f"/offset/{offset}" + + if kwargs is not None: + for operator, value in kwargs.items(): + if value is not None: + api_call += f"/{operator}/{value}" + if data_id is not None: + api_call += f"/data_id/{','.join([str(int(i)) for i in data_id])}" + return self.__list_datasets(api_call=api_call) + + def __list_datasets(self, api_call: str) -> pd.DataFrame: + xml_string = self._http.get(api_call).text + datasets_dict = xmltodict.parse(xml_string, force_list=("oml:dataset",)) + + # Minimalistic check if the XML is useful + assert isinstance(datasets_dict["oml:data"]["oml:dataset"], list), type( + datasets_dict["oml:data"], + ) + assert datasets_dict["oml:data"]["@xmlns:oml"] == "http://openml.org/openml", datasets_dict[ + "oml:data" + ]["@xmlns:oml"] + + datasets = {} + for dataset_ in datasets_dict["oml:data"]["oml:dataset"]: + ignore_attribute = ["oml:file_id", "oml:quality"] + dataset = { + k.replace("oml:", ""): v for (k, v) in dataset_.items() if k not in ignore_attribute + } + dataset["did"] = int(dataset["did"]) + dataset["version"] = int(dataset["version"]) + + # The number of qualities can range from 0 to infinity + for quality in dataset_.get("oml:quality", []): + try: + dataset[quality["@name"]] = int(quality["#text"]) + except ValueError: + dataset[quality["@name"]] = float(quality["#text"]) + datasets[dataset["did"]] = dataset + + return pd.DataFrame.from_dict(datasets, orient="index").astype( + { + "did": int, + "version": int, + "status": pd.CategoricalDtype(["active", "deactivated", "in_preparation"]), + } + ) + + def _create_dataset_from_xml(self, xml: str) -> OpenMLDataset: + """Create a dataset given a xml string. + + Parameters + ---------- + xml : string + Dataset xml representation. + + Returns + ------- + OpenMLDataset + """ + description = xmltodict.parse(xml)["oml:data_set_description"] + + # TODO file path after download, cache_format default = 'pickle' + arff_file = None + features_file = None + parquet_file = None + qualities_file = None + + return OpenMLDataset( + description["oml:name"], + description.get("oml:description"), + data_format=description["oml:format"], + dataset_id=int(description["oml:id"]), + version=int(description["oml:version"]), + creator=description.get("oml:creator"), + contributor=description.get("oml:contributor"), + collection_date=description.get("oml:collection_date"), + upload_date=description.get("oml:upload_date"), + language=description.get("oml:language"), + licence=description.get("oml:licence"), + url=description["oml:url"], + default_target_attribute=description.get("oml:default_target_attribute"), + row_id_attribute=description.get("oml:row_id_attribute"), + ignore_attribute=description.get("oml:ignore_attribute"), + version_label=description.get("oml:version_label"), + citation=description.get("oml:citation"), + tag=description.get("oml:tag"), + visibility=description.get("oml:visibility"), + original_data_url=description.get("oml:original_data_url"), + paper_url=description.get("oml:paper_url"), + update_comment=description.get("oml:update_comment"), + md5_checksum=description.get("oml:md5_checksum"), + data_file=str(arff_file) if arff_file is not None else None, + features_file=str(features_file) if features_file is not None else None, + qualities_file=str(qualities_file) if qualities_file is not None else None, + parquet_url=description.get("oml:parquet_url"), + parquet_file=str(parquet_file) if parquet_file is not None else None, + ) class DatasetsV2(DatasetsAPI): - def get(self, dataset_id: int) -> OpenMLDataset | tuple[OpenMLDataset, Response]: - raise NotImplementedError + def get( + self, dataset_id: int, *, return_response: bool = False + ) -> OpenMLDataset | tuple[OpenMLDataset, Response]: + path = f"datasets/{dataset_id}" + response = self._http.get(path) + json_content = response.json() + dataset = self._create_dataset_from_json(json_content) + + if return_response: + return dataset, response + + return dataset + + def list( # noqa: PLR0913 + self, + data_id: list[int] | None = None, + offset: int | None = None, + size: int | None = None, + status: str | None = None, + tag: str | None = None, + data_name: str | None = None, + data_version: int | None = None, + number_instances: int | str | None = None, + number_features: int | str | None = None, + number_classes: int | str | None = None, + number_missing_values: int | str | None = None, + ) -> pd.DataFrame: + """Return a dataframe of all dataset which are on OpenML. + + Supports large amount of results. + + Parameters + ---------- + data_id : list, optional + A list of data ids, to specify which datasets should be + listed + offset : int, optional + The number of datasets to skip, starting from the first. + size : int, optional + The maximum number of datasets to show. + status : str, optional + Should be {active, in_preparation, deactivated}. By + default active datasets are returned, but also datasets + from another status can be requested. + tag : str, optional + data_name : str, optional + data_version : int, optional + number_instances : int | str, optional + number_features : int | str, optional + number_classes : int | str, optional + number_missing_values : int | str, optional + + Returns + ------- + datasets: dataframe + Each row maps to a dataset + Each column contains the following information: + - dataset id + - name + - format + - status + If qualities are calculated for the dataset, some of + these are also included as columns. + """ + listing_call = partial( + self._list_datasets, + data_id=data_id, + status=status, + tag=tag, + data_name=data_name, + data_version=data_version, + number_instances=number_instances, + number_features=number_features, + number_classes=number_classes, + number_missing_values=number_missing_values, + ) + batches = openml.utils._list_all(listing_call, offset=offset, limit=size) + if len(batches) == 0: + return pd.DataFrame() + + return pd.concat(batches) + + def _list_datasets( + self, + limit: int, + offset: int, + **kwargs: Any, + ) -> pd.DataFrame: + """ + Perform api call to return a list of all datasets. + + Parameters + ---------- + The arguments that are lists are separated from the single value + ones which are put into the kwargs. + display_errors is also separated from the kwargs since it has a + default value. + + limit : int + The maximum number of datasets to show. + offset : int + The number of datasets to skip, starting from the first. + data_id : list, optional + + kwargs : dict, optional + Legal filter operators (keys in the dict): + tag, status, limit, offset, data_name, data_version, number_instances, + number_features, number_classes, number_missing_values, data_id. + + Returns + ------- + datasets : dataframe + """ + json: dict[str, Any] = {"pagination": {}} + + if limit is not None: + json["pagination"]["limit"] = limit + if offset is not None: + json["pagination"]["offset"] = offset + + if kwargs is not None: + for operator, value in kwargs.items(): + if value is not None: + json[operator] = value + + return self.__list_datasets(json=json) + + def __list_datasets(self, json: dict) -> pd.DataFrame: + api_call = "datasets/list" + datasets_list = self._http.post(api_call, json=json).json() + + # Minimalistic check if the JSON is useful + assert isinstance(datasets_list, list), type(datasets_list) + + datasets = {} + for dataset_ in datasets_list: + ignore_attribute = ["file_id", "quality"] + dataset = {k: v for (k, v) in dataset_.items() if k not in ignore_attribute} + dataset["did"] = int(dataset["did"]) + dataset["version"] = int(dataset["version"]) + + # The number of qualities can range from 0 to infinity + for quality in dataset_.get("quality", []): + try: + dataset[quality["name"]] = int(quality["text"]) + except ValueError: + dataset[quality["name"]] = float(quality["text"]) + datasets[dataset["did"]] = dataset + + return pd.DataFrame.from_dict(datasets, orient="index").astype( + { + "did": int, + "version": int, + "status": pd.CategoricalDtype(["active", "deactivated", "in_preparation"]), + } + ) + + def _create_dataset_from_json(self, json_content: dict) -> OpenMLDataset: + """Create a dataset given a json. + + Parameters + ---------- + json_content : dict + Dataset dict/json representation. + + Returns + ------- + OpenMLDataset + """ + # TODO file path after download, cache_format default = 'pickle' + arff_file = None + features_file = None + parquet_file = None + qualities_file = None + + return OpenMLDataset( + json_content["name"], + json_content.get("description"), + data_format=json_content["format"], + dataset_id=int(json_content["id"]), + version=int(json_content["version"]), + creator=json_content.get("creator"), + contributor=json_content.get("contributor"), + collection_date=json_content.get("collection_date"), + upload_date=json_content.get("upload_date"), + language=json_content.get("language"), + licence=json_content.get("licence"), + url=json_content["url"], + default_target_attribute=json_content.get("default_target_attribute"), + row_id_attribute=json_content.get("row_id_attribute"), + ignore_attribute=json_content.get("ignore_attribute"), + version_label=json_content.get("version_label"), + citation=json_content.get("citation"), + tag=json_content.get("tag"), + visibility=json_content.get("visibility"), + original_data_url=json_content.get("original_data_url"), + paper_url=json_content.get("paper_url"), + update_comment=json_content.get("update_comment"), + md5_checksum=json_content.get("md5_checksum"), + data_file=str(arff_file) if arff_file is not None else None, + features_file=str(features_file) if features_file is not None else None, + qualities_file=str(qualities_file) if qualities_file is not None else None, + parquet_url=json_content.get("parquet_url"), + parquet_file=str(parquet_file) if parquet_file is not None else None, + ) From 5dfcbce55a027d19cd502ea7bb3d521c2b1bca29 Mon Sep 17 00:00:00 2001 From: geetu040 Date: Wed, 7 Jan 2026 22:14:31 +0500 Subject: [PATCH 04/10] refactor --- openml/_api/config.py | 62 +++++++++++++++++++++++++++++++++++-- openml/_api/http/client.py | 18 +++++++---- openml/_api/runtime/core.py | 9 ++---- 3 files changed, 74 insertions(+), 15 deletions(-) diff --git a/openml/_api/config.py b/openml/_api/config.py index bd93c3cad..1431f66b1 100644 --- a/openml/_api/config.py +++ b/openml/_api/config.py @@ -1,5 +1,61 @@ from __future__ import annotations -API_V1_SERVER = "https://www.openml.org/api/v1/xml" -API_V2_SERVER = "http://127.0.0.1:8001" -API_KEY = "..." +from dataclasses import dataclass +from typing import Literal + +DelayMethod = Literal["human", "robot"] + + +@dataclass +class APIConfig: + server: str + base_url: str + key: str + + +@dataclass +class APISettings: + v1: APIConfig + v2: APIConfig + + +@dataclass +class ConnectionConfig: + retries: int = 3 + delay_method: DelayMethod = "human" + delay_time: int = 1 # seconds + + def __post_init__(self) -> None: + if self.delay_method not in ("human", "robot"): + raise ValueError(f"delay_method must be 'human' or 'robot', got {self.delay_method}") + + +@dataclass +class CacheConfig: + dir: str = "~/.openml/cache" + ttl: int = 60 * 60 * 24 * 7 # one week + + +@dataclass +class Settings: + api: APISettings + connection: ConnectionConfig + cache: CacheConfig + + +settings = Settings( + api=APISettings( + v1=APIConfig( + server="https://www.openml.org/", + base_url="api/v1/xml/", + key="...", + ), + v2=APIConfig( + server="http://127.0.0.1:8001/", + base_url="", + key="...", + ), + ), + connection=ConnectionConfig(), + cache=CacheConfig(), +) diff --git a/openml/_api/http/client.py b/openml/_api/http/client.py index dea5de809..74e08c709 100644 --- a/openml/_api/http/client.py +++ b/openml/_api/http/client.py @@ -1,24 +1,30 @@ from __future__ import annotations -from typing import Any, Mapping +from typing import TYPE_CHECKING, Any, Mapping import requests from requests import Response from openml.__version__ import __version__ +if TYPE_CHECKING: + from openml._api.config import APIConfig + class HTTPClient: - def __init__(self, base_url: str) -> None: - self.base_url = base_url + def __init__(self, config: APIConfig) -> None: + self.config = config self.headers: dict[str, str] = {"user-agent": f"openml-python/{__version__}"} + def _create_url(self, path: str) -> str: + return self.config.server + self.config.base_url + path + def get( self, path: str, params: Mapping[str, Any] | None = None, ) -> Response: - url = f"{self.base_url}/{path}" + url = self._create_url(path) return requests.get(url, params=params, headers=self.headers, timeout=10) def post( @@ -27,7 +33,7 @@ def post( data: Mapping[str, Any] | None = None, files: Any = None, ) -> Response: - url = f"{self.base_url}/{path}" + url = self._create_url(path) return requests.post(url, data=data, files=files, headers=self.headers, timeout=10) def delete( @@ -35,5 +41,5 @@ def delete( path: str, params: Mapping[str, Any] | None = None, ) -> Response: - url = f"{self.base_url}/{path}" + url = self._create_url(path) return requests.delete(url, params=params, headers=self.headers, timeout=10) diff --git a/openml/_api/runtime/core.py b/openml/_api/runtime/core.py index aa09a69db..98b587411 100644 --- a/openml/_api/runtime/core.py +++ b/openml/_api/runtime/core.py @@ -2,10 +2,7 @@ from typing import TYPE_CHECKING -from openml._api.config import ( - API_V1_SERVER, - API_V2_SERVER, -) +from openml._api.config import settings from openml._api.http.client import HTTPClient from openml._api.resources import ( DatasetsV1, @@ -25,8 +22,8 @@ def __init__(self, *, datasets: DatasetsAPI, tasks: TasksAPI): def build_backend(version: str, *, strict: bool) -> APIBackend: - v1_http = HTTPClient(API_V1_SERVER) - v2_http = HTTPClient(API_V2_SERVER) + v1_http = HTTPClient(config=settings.api.v1) + v2_http = HTTPClient(config=settings.api.v2) v1 = APIBackend( datasets=DatasetsV1(v1_http), From 2acbe9992cf95bfc103ff4fa0c360a58c1842870 Mon Sep 17 00:00:00 2001 From: geetu040 Date: Wed, 7 Jan 2026 22:24:03 +0500 Subject: [PATCH 05/10] implement cache_dir --- openml/_api/http/client.py | 74 +++++++++++++++++++++++++++++++++----- 1 file changed, 66 insertions(+), 8 deletions(-) diff --git a/openml/_api/http/client.py b/openml/_api/http/client.py index 74e08c709..49b05c88e 100644 --- a/openml/_api/http/client.py +++ b/openml/_api/http/client.py @@ -1,36 +1,93 @@ from __future__ import annotations -from typing import TYPE_CHECKING, Any, Mapping +from pathlib import Path +from typing import TYPE_CHECKING, Any +from urllib.parse import urlencode, urljoin, urlparse import requests from requests import Response from openml.__version__ import __version__ +from openml._api.config import settings if TYPE_CHECKING: from openml._api.config import APIConfig -class HTTPClient: +class CacheMixin: + @property + def dir(self) -> str: + return settings.cache.dir + + @property + def ttl(self) -> int: + return settings.cache.ttl + + def _get_cache_directory(self, url: str, params: dict[str, Any]) -> Path: + parsed_url = urlparse(url) + netloc_parts = parsed_url.netloc.split(".")[::-1] # reverse domain + path_parts = parsed_url.path.strip("/").split("/") + + # remove api_key and serialize params if any + filtered_params = {k: v for k, v in params.items() if k != "api_key"} + params_part = [urlencode(filtered_params)] if filtered_params else [] + + return Path(self.dir).joinpath(*netloc_parts, *path_parts, *params_part) + + def _get_cache_response(self, url: str, params: dict[str, Any]) -> Response | None: # noqa: ARG002 + return None + + def _set_cache_response(self, url: str, params: dict[str, Any], response: Response) -> None: # noqa: ARG002 + return None + + +class HTTPClient(CacheMixin): def __init__(self, config: APIConfig) -> None: self.config = config self.headers: dict[str, str] = {"user-agent": f"openml-python/{__version__}"} - def _create_url(self, path: str) -> str: - return self.config.server + self.config.base_url + path + @property + def server(self) -> str: + return self.config.server + + @property + def base_url(self) -> str: + return self.config.base_url + + def _create_url(self, path: str) -> Any: + return urljoin(self.server, urljoin(self.base_url, path)) def get( self, path: str, - params: Mapping[str, Any] | None = None, + *, + params: dict[str, Any] | None = None, + use_cache: bool = False, + use_api_key: bool = False, ) -> Response: url = self._create_url(path) - return requests.get(url, params=params, headers=self.headers, timeout=10) + params = dict(params) if params is not None else {} + + if use_api_key: + params["api_key"] = self.config.key + + if use_cache: + response = self._get_cache_response(url, params) + if response: + return response + + response = requests.get(url, params=params, headers=self.headers, timeout=10) + + if use_cache: + self._set_cache_response(url, params, response) + + return response def post( self, path: str, - data: Mapping[str, Any] | None = None, + *, + data: dict[str, Any] | None = None, files: Any = None, ) -> Response: url = self._create_url(path) @@ -39,7 +96,8 @@ def post( def delete( self, path: str, - params: Mapping[str, Any] | None = None, + *, + params: dict[str, Any] | None = None, ) -> Response: url = self._create_url(path) return requests.delete(url, params=params, headers=self.headers, timeout=10) From af99880a9e16a49833c63084c9e9267c112b6b91 Mon Sep 17 00:00:00 2001 From: geetu040 Date: Wed, 7 Jan 2026 23:42:17 +0500 Subject: [PATCH 06/10] refactor --- openml/_api/config.py | 1 + openml/_api/http/client.py | 100 +++++++++++++++++++++++++++---------- 2 files changed, 75 insertions(+), 26 deletions(-) diff --git a/openml/_api/config.py b/openml/_api/config.py index 1431f66b1..848fe8da1 100644 --- a/openml/_api/config.py +++ b/openml/_api/config.py @@ -11,6 +11,7 @@ class APIConfig: server: str base_url: str key: str + timeout: int = 10 # seconds @dataclass diff --git a/openml/_api/http/client.py b/openml/_api/http/client.py index 49b05c88e..a90e93933 100644 --- a/openml/_api/http/client.py +++ b/openml/_api/http/client.py @@ -23,7 +23,7 @@ def dir(self) -> str: def ttl(self) -> int: return settings.cache.ttl - def _get_cache_directory(self, url: str, params: dict[str, Any]) -> Path: + def _get_cache_dir(self, url: str, params: dict[str, Any]) -> Path: parsed_url = urlparse(url) netloc_parts = parsed_url.netloc.split(".")[::-1] # reverse domain path_parts = parsed_url.path.strip("/").split("/") @@ -34,10 +34,10 @@ def _get_cache_directory(self, url: str, params: dict[str, Any]) -> Path: return Path(self.dir).joinpath(*netloc_parts, *path_parts, *params_part) - def _get_cache_response(self, url: str, params: dict[str, Any]) -> Response | None: # noqa: ARG002 - return None + def _get_cache_response(self, cache_dir: Path) -> Response: # noqa: ARG002 + return Response() - def _set_cache_response(self, url: str, params: dict[str, Any], response: Response) -> None: # noqa: ARG002 + def _set_cache_response(self, cache_dir: Path, response: Response) -> None: # noqa: ARG002 return None @@ -54,50 +54,98 @@ def server(self) -> str: def base_url(self) -> str: return self.config.base_url - def _create_url(self, path: str) -> Any: - return urljoin(self.server, urljoin(self.base_url, path)) + @property + def key(self) -> str: + return self.config.key - def get( + @property + def timeout(self) -> int: + return self.config.timeout + + def request( self, + method: str, path: str, *, - params: dict[str, Any] | None = None, use_cache: bool = False, use_api_key: bool = False, + **request_kwargs: Any, ) -> Response: - url = self._create_url(path) - params = dict(params) if params is not None else {} + url = urljoin(self.server, urljoin(self.base_url, path)) + params = request_kwargs.pop("params", {}) + params = params.copy() if use_api_key: - params["api_key"] = self.config.key + params["api_key"] = self.key - if use_cache: - response = self._get_cache_response(url, params) - if response: - return response + headers = request_kwargs.pop("headers", {}) + headers = headers.copy() + headers.update(self.headers) + + timeout = request_kwargs.pop("timeout", self.timeout) + cache_dir = self._get_cache_dir(url, params) - response = requests.get(url, params=params, headers=self.headers, timeout=10) + if use_cache: + try: + return self._get_cache_response(cache_dir) + # TODO: handle ttl expired error + except Exception: + raise + + response = requests.request( + method=method, + url=url, + params=params, + headers=headers, + timeout=timeout, + **request_kwargs, + ) if use_cache: - self._set_cache_response(url, params, response) + self._set_cache_response(cache_dir, response) return response - def post( + def get( self, path: str, *, - data: dict[str, Any] | None = None, - files: Any = None, + use_cache: bool = False, + use_api_key: bool = False, + **request_kwargs: Any, ) -> Response: - url = self._create_url(path) - return requests.post(url, data=data, files=files, headers=self.headers, timeout=10) + # TODO: remove override when cache is implemented + use_cache = False + return self.request( + method="GET", + path=path, + use_cache=use_cache, + use_api_key=use_api_key, + **request_kwargs, + ) + + def post( + self, + path: str, + **request_kwargs: Any, + ) -> Response: + return self.request( + method="POST", + path=path, + use_cache=False, + use_api_key=True, + **request_kwargs, + ) def delete( self, path: str, - *, - params: dict[str, Any] | None = None, + **request_kwargs: Any, ) -> Response: - url = self._create_url(path) - return requests.delete(url, params=params, headers=self.headers, timeout=10) + return self.request( + method="DELETE", + path=path, + use_cache=False, + use_api_key=True, + **request_kwargs, + ) From 8964517d5fa9b656dc1473adfc09e9a56c524073 Mon Sep 17 00:00:00 2001 From: JATAYU000 Date: Thu, 8 Jan 2026 09:14:49 +0530 Subject: [PATCH 07/10] edit, fork, delete updated --- openml/_api/resources/base.py | 36 +++- openml/_api/resources/datasets.py | 278 +++++++++++++++++++++++++++++- 2 files changed, 304 insertions(+), 10 deletions(-) diff --git a/openml/_api/resources/base.py b/openml/_api/resources/base.py index 9d480b06a..5a74239d1 100644 --- a/openml/_api/resources/base.py +++ b/openml/_api/resources/base.py @@ -2,6 +2,7 @@ from abc import ABC, abstractmethod from typing import TYPE_CHECKING +from typing_extensions import Literal if TYPE_CHECKING: import pandas as pd @@ -20,13 +21,18 @@ def __init__(self, http: HTTPClient): class DatasetsAPI(ResourceAPI, ABC): @abstractmethod def get( - self, dataset_id: int, *, return_response: bool + self, + dataset_id: int | str, + version: int | None = None, + error_if_multiple: bool = False, # noqa: FBT002, FBT001 + *, + return_response: bool = False, ) -> OpenMLDataset | tuple[OpenMLDataset, Response]: ... @abstractmethod def list( # noqa: PLR0913 self, - data_id: list[int] | None = None, + data_id: list[int] | None = None, # type: ignore offset: int | None = None, size: int | None = None, status: str | None = None, @@ -39,6 +45,32 @@ def list( # noqa: PLR0913 number_missing_values: int | str | None = None, ) -> pd.DataFrame: ... + @abstractmethod + def delete(self, dataset_id: int) -> bool: ... + + @abstractmethod + def edit( # noqa: PLR0913 + self, + data_id: int, + description: str | None = None, + creator: str | None = None, + contributor: str | None = None, + collection_date: str | None = None, + language: str | None = None, + default_target_attribute: str | None = None, + ignore_attribute: str | list[str] | None = None, # type: ignore + citation: str | None = None, + row_id_attribute: str | None = None, + original_data_url: str | None = None, + paper_url: str | None = None, + ) -> int: ... + + @abstractmethod + def fork(self, data_id: int) -> int: ... + + @abstractmethod + def status_update(self, data_id: int, status: Literal["active", "deactivated"]) -> None: ... + def _name_to_id( self, dataset_name: str, diff --git a/openml/_api/resources/datasets.py b/openml/_api/resources/datasets.py index f985cd75a..5414fba43 100644 --- a/openml/_api/resources/datasets.py +++ b/openml/_api/resources/datasets.py @@ -1,7 +1,9 @@ from __future__ import annotations +from collections import OrderedDict from functools import partial from typing import TYPE_CHECKING, Any +from typing_extensions import Literal if TYPE_CHECKING: from requests import Response @@ -16,11 +18,23 @@ class DatasetsV1(DatasetsAPI): def get( - self, dataset_id: int, *, return_response: bool = False + self, + dataset_id: int | str, + version: int | None = None, + error_if_multiple: bool = False, # noqa: FBT002, FBT001 + *, + return_response: bool = False, ) -> OpenMLDataset | tuple[OpenMLDataset, Response]: - path = f"data/{dataset_id}" + if isinstance(dataset_id, int): + resolved_id = dataset_id + elif dataset_id.isdigit(): + resolved_id = int(dataset_id) + else: + resolved_id = self._name_to_id(dataset_id, version, error_if_multiple) + + path = f"data/{resolved_id}" response = self._http.get(path) - xml_content = response.text # .text returns str, .content returns bytes + xml_content = response.text dataset = self._create_dataset_from_xml(xml_content) if return_response: @@ -97,6 +111,194 @@ def list( # noqa: PLR0913 return pd.concat(batches) + def delete(self, dataset_id: int) -> bool: + """Delete dataset with id `dataset_id` from the OpenML server. + + This can only be done if you are the owner of the dataset and + no tasks are attached to the dataset. + + Parameters + ---------- + dataset_id : int + OpenML id of the dataset + + Returns + ------- + bool + True if the deletion was successful. False otherwise. + """ + return openml.utils._delete_entity("data", dataset_id) + + def edit( # noqa: PLR0913 + self, + data_id: int, + description: str | None = None, + creator: str | None = None, + contributor: str | None = None, + collection_date: str | None = None, + language: str | None = None, + default_target_attribute: str | None = None, + ignore_attribute: str | list[str] | None = None, # type: ignore + citation: str | None = None, + row_id_attribute: str | None = None, + original_data_url: str | None = None, + paper_url: str | None = None, + ) -> int: + """Edits an OpenMLDataset. + + In addition to providing the dataset id of the dataset to edit (through data_id), + you must specify a value for at least one of the optional function arguments, + i.e. one value for a field to edit. + + This function allows editing of both non-critical and critical fields. + Critical fields are default_target_attribute, ignore_attribute, row_id_attribute. + + - Editing non-critical data fields is allowed for all authenticated users. + - Editing critical fields is allowed only for the owner, provided there are no tasks + associated with this dataset. + + If dataset has tasks or if the user is not the owner, the only way + to edit critical fields is to use fork_dataset followed by edit_dataset. + + Parameters + ---------- + data_id : int + ID of the dataset. + description : str + Description of the dataset. + creator : str + The person who created the dataset. + contributor : str + People who contributed to the current version of the dataset. + collection_date : str + The date the data was originally collected, given by the uploader. + language : str + Language in which the data is represented. + Starts with 1 upper case letter, rest lower case, e.g. 'English'. + default_target_attribute : str + The default target attribute, if it exists. + Can have multiple values, comma separated. + ignore_attribute : str | list + Attributes that should be excluded in modelling, + such as identifiers and indexes. + citation : str + Reference(s) that should be cited when building on this data. + row_id_attribute : str, optional + The attribute that represents the row-id column, if present in the + dataset. If ``data`` is a dataframe and ``row_id_attribute`` is not + specified, the index of the dataframe will be used as the + ``row_id_attribute``. If the name of the index is ``None``, it will + be discarded. + + .. versionadded: 0.8 + Inference of ``row_id_attribute`` from a dataframe. + original_data_url : str, optional + For derived data, the url to the original dataset. + paper_url : str, optional + Link to a paper describing the dataset. + + Returns + ------- + Dataset id + """ + if not isinstance(data_id, int): + raise TypeError(f"`data_id` must be of type `int`, not {type(data_id)}.") + + # compose data edit parameters as xml + form_data = {"data_id": data_id} # type: openml._api_calls.DATA_TYPE + xml = OrderedDict() # type: 'OrderedDict[str, OrderedDict]' + xml["oml:data_edit_parameters"] = OrderedDict() + xml["oml:data_edit_parameters"]["@xmlns:oml"] = "http://openml.org/openml" + xml["oml:data_edit_parameters"]["oml:description"] = description + xml["oml:data_edit_parameters"]["oml:creator"] = creator + xml["oml:data_edit_parameters"]["oml:contributor"] = contributor + xml["oml:data_edit_parameters"]["oml:collection_date"] = collection_date + xml["oml:data_edit_parameters"]["oml:language"] = language + xml["oml:data_edit_parameters"]["oml:default_target_attribute"] = default_target_attribute + xml["oml:data_edit_parameters"]["oml:row_id_attribute"] = row_id_attribute + xml["oml:data_edit_parameters"]["oml:ignore_attribute"] = ignore_attribute + xml["oml:data_edit_parameters"]["oml:citation"] = citation + xml["oml:data_edit_parameters"]["oml:original_data_url"] = original_data_url + xml["oml:data_edit_parameters"]["oml:paper_url"] = paper_url + + # delete None inputs + for k in list(xml["oml:data_edit_parameters"]): + if not xml["oml:data_edit_parameters"][k]: + del xml["oml:data_edit_parameters"][k] + + file_elements = { + "edit_parameters": ("description.xml", xmltodict.unparse(xml)), + } # type: openml._api_calls.FILE_ELEMENTS_TYPE + result_xml = self._http.post("data/edit", data=form_data, files=file_elements).text + result = xmltodict.parse(result_xml) + data_id = result["oml:data_edit"]["oml:id"] + return int(data_id) + + def fork(self, data_id: int) -> int: + """ + Creates a new dataset version, with the authenticated user as the new owner. + The forked dataset can have distinct dataset meta-data, + but the actual data itself is shared with the original version. + + This API is intended for use when a user is unable to edit the critical fields of a dataset + through the edit_dataset API. + (Critical fields are default_target_attribute, ignore_attribute, row_id_attribute.) + + Specifically, this happens when the user is: + 1. Not the owner of the dataset. + 2. User is the owner of the dataset, but the dataset has tasks. + + In these two cases the only way to edit critical fields is: + 1. STEP 1: Fork the dataset using fork_dataset API + 2. STEP 2: Call edit_dataset API on the forked version. + + + Parameters + ---------- + data_id : int + id of the dataset to be forked + + Returns + ------- + Dataset id of the forked dataset + + """ + if not isinstance(data_id, int): + raise TypeError(f"`data_id` must be of type `int`, not {type(data_id)}.") + # compose data fork parameters + form_data = {"data_id": data_id} + result_xml = self._http.post("data/fork", data=form_data).text + result = xmltodict.parse(result_xml) + data_id = result["oml:data_fork"]["oml:id"] + return int(data_id) + + def status_update(self, data_id: int, status: Literal["active", "deactivated"]) -> None: + """ + Updates the status of a dataset to either 'active' or 'deactivated'. + Please see the OpenML API documentation for a description of the status + and all legal status transitions: + https://docs.openml.org/concepts/data/#dataset-status + + Parameters + ---------- + data_id : int + The data id of the dataset + status : str, + 'active' or 'deactivated' + """ + legal_status = {"active", "deactivated"} + if status not in legal_status: + raise ValueError(f"Illegal status value. Legal values: {legal_status}") + + data: openml._api_calls.DATA_TYPE = {"data_id": data_id, "status": status} + result_xml = self._http.post("data/status/update", data=data).text + result = xmltodict.parse(result_xml) + server_data_id = result["oml:data_status_update"]["oml:id"] + server_status = result["oml:data_status_update"]["oml:status"] + if status != server_status or int(data_id) != int(server_data_id): + # This should never happen + raise ValueError("Data id/status does not collide") + def _list_datasets( self, limit: int, @@ -236,9 +438,21 @@ def _create_dataset_from_xml(self, xml: str) -> OpenMLDataset: class DatasetsV2(DatasetsAPI): def get( - self, dataset_id: int, *, return_response: bool = False + self, + dataset_id: int | str, + version: int | None = None, + error_if_multiple: bool = False, # noqa: FBT002, FBT001 + *, + return_response: bool = False, ) -> OpenMLDataset | tuple[OpenMLDataset, Response]: - path = f"datasets/{dataset_id}" + if isinstance(dataset_id, int): + resolved_id = dataset_id + elif dataset_id.isdigit(): + resolved_id = int(dataset_id) + else: + resolved_id = self._name_to_id(dataset_id, version, error_if_multiple) + + path = f"data/{resolved_id}" response = self._http.get(path) json_content = response.json() dataset = self._create_dataset_from_json(json_content) @@ -317,6 +531,55 @@ def list( # noqa: PLR0913 return pd.concat(batches) + def delete(self, dataset_id: int) -> bool: + raise NotImplementedError() + + def edit( # noqa: PLR0913 + self, + data_id: int, + description: str | None = None, + creator: str | None = None, + contributor: str | None = None, + collection_date: str | None = None, + language: str | None = None, + default_target_attribute: str | None = None, + ignore_attribute: str | list[str] | None = None, # type: ignore + citation: str | None = None, + row_id_attribute: str | None = None, + original_data_url: str | None = None, + paper_url: str | None = None, + ) -> int: + raise NotImplementedError() + + def fork(self, data_id: int) -> int: + raise NotImplementedError() + + def status_update(self, data_id: int, status: Literal["active", "deactivated"]) -> None: + """ + Updates the status of a dataset to either 'active' or 'deactivated'. + Please see the OpenML API documentation for a description of the status + and all legal status transitions: + https://docs.openml.org/concepts/data/#dataset-status + + Parameters + ---------- + data_id : int + The data id of the dataset + status : str, + 'active' or 'deactivated' + """ + legal_status = {"active", "deactivated"} + if status not in legal_status: + raise ValueError(f"Illegal status value. Legal values: {legal_status}") + + data: openml._api_calls.DATA_TYPE = {"dataset_id": data_id, "status": status} + result = self._http.post("datasets/status/update", json=data).json() + server_data_id = result["dataset_id"] + server_status = result["status"] + if status != server_status or int(data_id) != int(server_data_id): + # This should never happen + raise ValueError("Data id/status does not collide") + def _list_datasets( self, limit: int, @@ -365,7 +628,6 @@ def _list_datasets( def __list_datasets(self, json: dict) -> pd.DataFrame: api_call = "datasets/list" datasets_list = self._http.post(api_call, json=json).json() - # Minimalistic check if the JSON is useful assert isinstance(datasets_list, list), type(datasets_list) @@ -379,9 +641,9 @@ def __list_datasets(self, json: dict) -> pd.DataFrame: # The number of qualities can range from 0 to infinity for quality in dataset_.get("quality", []): try: - dataset[quality["name"]] = int(quality["text"]) + dataset[quality["name"]] = int(quality["value"]) except ValueError: - dataset[quality["name"]] = float(quality["text"]) + dataset[quality["name"]] = float(quality["value"]) datasets[dataset["did"]] = dataset return pd.DataFrame.from_dict(datasets, orient="index").astype( From 1c2fa9996aa0024af93ab1819877836b6ab803f2 Mon Sep 17 00:00:00 2001 From: JATAYU000 Date: Thu, 8 Jan 2026 15:57:09 +0530 Subject: [PATCH 08/10] Added features, updated list --- openml/_api/resources/base.py | 76 +---- openml/_api/resources/datasets.py | 494 +++++++++++++----------------- 2 files changed, 234 insertions(+), 336 deletions(-) diff --git a/openml/_api/resources/base.py b/openml/_api/resources/base.py index 5a74239d1..990dda998 100644 --- a/openml/_api/resources/base.py +++ b/openml/_api/resources/base.py @@ -1,7 +1,7 @@ from __future__ import annotations from abc import ABC, abstractmethod -from typing import TYPE_CHECKING +from typing import TYPE_CHECKING, Any from typing_extensions import Literal if TYPE_CHECKING: @@ -23,26 +23,18 @@ class DatasetsAPI(ResourceAPI, ABC): def get( self, dataset_id: int | str, - version: int | None = None, - error_if_multiple: bool = False, # noqa: FBT002, FBT001 *, return_response: bool = False, ) -> OpenMLDataset | tuple[OpenMLDataset, Response]: ... @abstractmethod - def list( # noqa: PLR0913 + def list( self, + limit: int, + offset: int, + *, data_id: list[int] | None = None, # type: ignore - offset: int | None = None, - size: int | None = None, - status: str | None = None, - tag: str | None = None, - data_name: str | None = None, - data_version: int | None = None, - number_instances: int | str | None = None, - number_features: int | str | None = None, - number_classes: int | str | None = None, - number_missing_values: int | str | None = None, + **kwargs: Any, ) -> pd.DataFrame: ... @abstractmethod @@ -71,54 +63,14 @@ def fork(self, data_id: int) -> int: ... @abstractmethod def status_update(self, data_id: int, status: Literal["active", "deactivated"]) -> None: ... - def _name_to_id( - self, - dataset_name: str, - version: int | None = None, - error_if_multiple: bool = False, # noqa: FBT001, FBT002 - ) -> int: - """Attempt to find the dataset id of the dataset with the given name. - - If multiple datasets with the name exist, and ``error_if_multiple`` is ``False``, - then return the least recent still active dataset. - - Raises an error if no dataset with the name is found. - Raises an error if a version is specified but it could not be found. - - Parameters - ---------- - dataset_name : str - The name of the dataset for which to find its id. - version : int, optional - Version to retrieve. If not specified, the oldest active version is returned. - error_if_multiple : bool (default=False) - If `False`, if multiple datasets match, return the least recent active dataset. - If `True`, if multiple datasets match, raise an error. - download_qualities : bool, optional (default=True) - If `True`, also download qualities.xml file. If False it skip the qualities.xml. - - Returns - ------- - int - The id of the dataset. - """ - status = None if version is not None else "active" - candidates = self.list( - data_name=dataset_name, - status=status, - data_version=version, - ) - if error_if_multiple and len(candidates) > 1: - msg = f"Multiple active datasets exist with name '{dataset_name}'." - raise ValueError(msg) - - if candidates.empty: - no_dataset_for_name = f"No active datasets exist with name '{dataset_name}'" - and_version = f" and version '{version}'." if version is not None else "." - raise RuntimeError(no_dataset_for_name + and_version) - - # Dataset ids are chronological so we can just sort based on ids (instead of version) - return candidates["did"].min() # type: ignore + @abstractmethod + def list_qualities(self) -> list[str]: ... # type: ignore + + @abstractmethod + def feature_add_ontology(self, data_id: int, index: int, ontology: str) -> bool: ... + + @abstractmethod + def feature_remove_ontology(self, data_id: int, index: int, ontology: str) -> bool: ... class TasksAPI(ResourceAPI, ABC): diff --git a/openml/_api/resources/datasets.py b/openml/_api/resources/datasets.py index 5414fba43..845212b20 100644 --- a/openml/_api/resources/datasets.py +++ b/openml/_api/resources/datasets.py @@ -1,7 +1,6 @@ from __future__ import annotations from collections import OrderedDict -from functools import partial from typing import TYPE_CHECKING, Any from typing_extensions import Literal @@ -20,19 +19,10 @@ class DatasetsV1(DatasetsAPI): def get( self, dataset_id: int | str, - version: int | None = None, - error_if_multiple: bool = False, # noqa: FBT002, FBT001 *, return_response: bool = False, ) -> OpenMLDataset | tuple[OpenMLDataset, Response]: - if isinstance(dataset_id, int): - resolved_id = dataset_id - elif dataset_id.isdigit(): - resolved_id = int(dataset_id) - else: - resolved_id = self._name_to_id(dataset_id, version, error_if_multiple) - - path = f"data/{resolved_id}" + path = f"data/{dataset_id}" response = self._http.get(path) xml_content = response.text dataset = self._create_dataset_from_xml(xml_content) @@ -42,74 +32,88 @@ def get( return dataset - def list( # noqa: PLR0913 + def list( self, - data_id: list[int] | None = None, - offset: int | None = None, - size: int | None = None, - status: str | None = None, - tag: str | None = None, - data_name: str | None = None, - data_version: int | None = None, - number_instances: int | str | None = None, - number_features: int | str | None = None, - number_classes: int | str | None = None, - number_missing_values: int | str | None = None, + limit: int, + offset: int, + *, + data_id: list[int] | None = None, # type: ignore + **kwargs: Any, ) -> pd.DataFrame: - """Return a dataframe of all dataset which are on OpenML. - - Supports large amount of results. + """ + Perform api call to return a list of all datasets. Parameters ---------- - data_id : list, optional - A list of data ids, to specify which datasets should be - listed - offset : int, optional - The number of datasets to skip, starting from the first. - size : int, optional + The arguments that are lists are separated from the single value + ones which are put into the kwargs. + display_errors is also separated from the kwargs since it has a + default value. + + limit : int The maximum number of datasets to show. - status : str, optional - Should be {active, in_preparation, deactivated}. By - default active datasets are returned, but also datasets - from another status can be requested. - tag : str, optional - data_name : str, optional - data_version : int, optional - number_instances : int | str, optional - number_features : int | str, optional - number_classes : int | str, optional - number_missing_values : int | str, optional + offset : int + The number of datasets to skip, starting from the first. + data_id : list, optional + + kwargs : dict, optional + Legal filter operators (keys in the dict): + tag, status, limit, offset, data_name, data_version, number_instances, + number_features, number_classes, number_missing_values. Returns ------- - datasets: dataframe - Each row maps to a dataset - Each column contains the following information: - - dataset id - - name - - format - - status - If qualities are calculated for the dataset, some of - these are also included as columns. + datasets : dataframe """ - listing_call = partial( - self._list_datasets, - data_id=data_id, - status=status, - tag=tag, - data_name=data_name, - data_version=data_version, - number_instances=number_instances, - number_features=number_features, - number_classes=number_classes, - number_missing_values=number_missing_values, + api_call = "data/list" + + if limit is not None: + api_call += f"/limit/{limit}" + if offset is not None: + api_call += f"/offset/{offset}" + + if kwargs is not None: + for operator, value in kwargs.items(): + if value is not None: + api_call += f"/{operator}/{value}" + if data_id is not None: + api_call += f"/data_id/{','.join([str(int(i)) for i in data_id])}" + + xml_string = self._http.get(api_call).text + datasets_dict = xmltodict.parse(xml_string, force_list=("oml:dataset",)) + + # Minimalistic check if the XML is useful + assert isinstance(datasets_dict["oml:data"]["oml:dataset"], list), type( + datasets_dict["oml:data"], ) - batches = openml.utils._list_all(listing_call, offset=offset, limit=size) - if len(batches) == 0: - return pd.DataFrame() + assert datasets_dict["oml:data"]["@xmlns:oml"] == "http://openml.org/openml", datasets_dict[ + "oml:data" + ]["@xmlns:oml"] + + datasets = {} + for dataset_ in datasets_dict["oml:data"]["oml:dataset"]: + ignore_attribute = ["oml:file_id", "oml:quality"] + dataset = { + k.replace("oml:", ""): v for (k, v) in dataset_.items() if k not in ignore_attribute + } + dataset["did"] = int(dataset["did"]) + dataset["version"] = int(dataset["version"]) + + # The number of qualities can range from 0 to infinity + for quality in dataset_.get("oml:quality", []): + try: + dataset[quality["@name"]] = int(quality["#text"]) + except ValueError: + dataset[quality["@name"]] = float(quality["#text"]) + datasets[dataset["did"]] = dataset - return pd.concat(batches) + return pd.DataFrame.from_dict(datasets, orient="index").astype( + { + "did": int, + "version": int, + "status": pd.CategoricalDtype(["active", "deactivated", "in_preparation"]), + } + ) def delete(self, dataset_id: int) -> bool: """Delete dataset with id `dataset_id` from the OpenML server. @@ -299,90 +303,27 @@ def status_update(self, data_id: int, status: Literal["active", "deactivated"]) # This should never happen raise ValueError("Data id/status does not collide") - def _list_datasets( - self, - limit: int, - offset: int, - *, - data_id: list[int] | None = None, # type: ignore - **kwargs: Any, - ) -> pd.DataFrame: - """ - Perform api call to return a list of all datasets. - - Parameters - ---------- - The arguments that are lists are separated from the single value - ones which are put into the kwargs. - display_errors is also separated from the kwargs since it has a - default value. + def list_qualities(self) -> list[str]: # type: ignore + """Return list of data qualities available. - limit : int - The maximum number of datasets to show. - offset : int - The number of datasets to skip, starting from the first. - data_id : list, optional - - kwargs : dict, optional - Legal filter operators (keys in the dict): - tag, status, limit, offset, data_name, data_version, number_instances, - number_features, number_classes, number_missing_values. + The function performs an API call to retrieve the entire list of + data qualities that are computed on the datasets uploaded. Returns ------- - datasets : dataframe + list """ - api_call = "data/list" - - if limit is not None: - api_call += f"/limit/{limit}" - if offset is not None: - api_call += f"/offset/{offset}" - - if kwargs is not None: - for operator, value in kwargs.items(): - if value is not None: - api_call += f"/{operator}/{value}" - if data_id is not None: - api_call += f"/data_id/{','.join([str(int(i)) for i in data_id])}" - return self.__list_datasets(api_call=api_call) - - def __list_datasets(self, api_call: str) -> pd.DataFrame: + api_call = "data/qualities/list" xml_string = self._http.get(api_call).text - datasets_dict = xmltodict.parse(xml_string, force_list=("oml:dataset",)) - + qualities = xmltodict.parse(xml_string, force_list=("oml:quality")) # Minimalistic check if the XML is useful - assert isinstance(datasets_dict["oml:data"]["oml:dataset"], list), type( - datasets_dict["oml:data"], - ) - assert datasets_dict["oml:data"]["@xmlns:oml"] == "http://openml.org/openml", datasets_dict[ - "oml:data" - ]["@xmlns:oml"] - - datasets = {} - for dataset_ in datasets_dict["oml:data"]["oml:dataset"]: - ignore_attribute = ["oml:file_id", "oml:quality"] - dataset = { - k.replace("oml:", ""): v for (k, v) in dataset_.items() if k not in ignore_attribute - } - dataset["did"] = int(dataset["did"]) - dataset["version"] = int(dataset["version"]) + if "oml:data_qualities_list" not in qualities: + raise ValueError('Error in return XML, does not contain "oml:data_qualities_list"') - # The number of qualities can range from 0 to infinity - for quality in dataset_.get("oml:quality", []): - try: - dataset[quality["@name"]] = int(quality["#text"]) - except ValueError: - dataset[quality["@name"]] = float(quality["#text"]) - datasets[dataset["did"]] = dataset + if not isinstance(qualities["oml:data_qualities_list"]["oml:quality"], list): + raise TypeError('Error in return XML, does not contain "oml:quality" as a list') - return pd.DataFrame.from_dict(datasets, orient="index").astype( - { - "did": int, - "version": int, - "status": pd.CategoricalDtype(["active", "deactivated", "in_preparation"]), - } - ) + return qualities["oml:data_qualities_list"]["oml:quality"] def _create_dataset_from_xml(self, xml: str) -> OpenMLDataset: """Create a dataset given a xml string. @@ -435,24 +376,74 @@ def _create_dataset_from_xml(self, xml: str) -> OpenMLDataset: parquet_file=str(parquet_file) if parquet_file is not None else None, ) + def feature_add_ontology(self, data_id: int, index: int, ontology: str) -> bool: + """ + An ontology describes the concept that are described in a feature. An + ontology is defined by an URL where the information is provided. Adds + an ontology (URL) to a given dataset feature (defined by a dataset id + and index). The dataset has to exists on OpenML and needs to have been + processed by the evaluation engine. + + Parameters + ---------- + data_id : int + id of the dataset to which the feature belongs + index : int + index of the feature in dataset (0-based) + ontology : str + URL to ontology (max. 256 characters) + + Returns + ------- + True or throws an OpenML server exception + """ + upload_data: dict[str, int | str] = { + "data_id": data_id, + "index": index, + "ontology": ontology, + } + self._http.post("data/feature/ontology/add", data=upload_data) + # an error will be thrown in case the request was unsuccessful + return True + + def feature_remove_ontology(self, data_id: int, index: int, ontology: str) -> bool: + """ + Removes an existing ontology (URL) from a given dataset feature (defined + by a dataset id and index). The dataset has to exists on OpenML and needs + to have been processed by the evaluation engine. Ontology needs to be + attached to the specific fearure. + + Parameters + ---------- + data_id : int + id of the dataset to which the feature belongs + index : int + index of the feature in dataset (0-based) + ontology : str + URL to ontology (max. 256 characters) + + Returns + ------- + True or throws an OpenML server exception + """ + upload_data: dict[str, int | str] = { + "data_id": data_id, + "index": index, + "ontology": ontology, + } + self._http.post("data/feature/ontology/remove", data=upload_data) + # an error will be thrown in case the request was unsuccessful + return True + class DatasetsV2(DatasetsAPI): def get( self, dataset_id: int | str, - version: int | None = None, - error_if_multiple: bool = False, # noqa: FBT002, FBT001 *, return_response: bool = False, ) -> OpenMLDataset | tuple[OpenMLDataset, Response]: - if isinstance(dataset_id, int): - resolved_id = dataset_id - elif dataset_id.isdigit(): - resolved_id = int(dataset_id) - else: - resolved_id = self._name_to_id(dataset_id, version, error_if_multiple) - - path = f"data/{resolved_id}" + path = f"data/{dataset_id}" response = self._http.get(path) json_content = response.json() dataset = self._create_dataset_from_json(json_content) @@ -462,125 +453,7 @@ def get( return dataset - def list( # noqa: PLR0913 - self, - data_id: list[int] | None = None, - offset: int | None = None, - size: int | None = None, - status: str | None = None, - tag: str | None = None, - data_name: str | None = None, - data_version: int | None = None, - number_instances: int | str | None = None, - number_features: int | str | None = None, - number_classes: int | str | None = None, - number_missing_values: int | str | None = None, - ) -> pd.DataFrame: - """Return a dataframe of all dataset which are on OpenML. - - Supports large amount of results. - - Parameters - ---------- - data_id : list, optional - A list of data ids, to specify which datasets should be - listed - offset : int, optional - The number of datasets to skip, starting from the first. - size : int, optional - The maximum number of datasets to show. - status : str, optional - Should be {active, in_preparation, deactivated}. By - default active datasets are returned, but also datasets - from another status can be requested. - tag : str, optional - data_name : str, optional - data_version : int, optional - number_instances : int | str, optional - number_features : int | str, optional - number_classes : int | str, optional - number_missing_values : int | str, optional - - Returns - ------- - datasets: dataframe - Each row maps to a dataset - Each column contains the following information: - - dataset id - - name - - format - - status - If qualities are calculated for the dataset, some of - these are also included as columns. - """ - listing_call = partial( - self._list_datasets, - data_id=data_id, - status=status, - tag=tag, - data_name=data_name, - data_version=data_version, - number_instances=number_instances, - number_features=number_features, - number_classes=number_classes, - number_missing_values=number_missing_values, - ) - batches = openml.utils._list_all(listing_call, offset=offset, limit=size) - if len(batches) == 0: - return pd.DataFrame() - - return pd.concat(batches) - - def delete(self, dataset_id: int) -> bool: - raise NotImplementedError() - - def edit( # noqa: PLR0913 - self, - data_id: int, - description: str | None = None, - creator: str | None = None, - contributor: str | None = None, - collection_date: str | None = None, - language: str | None = None, - default_target_attribute: str | None = None, - ignore_attribute: str | list[str] | None = None, # type: ignore - citation: str | None = None, - row_id_attribute: str | None = None, - original_data_url: str | None = None, - paper_url: str | None = None, - ) -> int: - raise NotImplementedError() - - def fork(self, data_id: int) -> int: - raise NotImplementedError() - - def status_update(self, data_id: int, status: Literal["active", "deactivated"]) -> None: - """ - Updates the status of a dataset to either 'active' or 'deactivated'. - Please see the OpenML API documentation for a description of the status - and all legal status transitions: - https://docs.openml.org/concepts/data/#dataset-status - - Parameters - ---------- - data_id : int - The data id of the dataset - status : str, - 'active' or 'deactivated' - """ - legal_status = {"active", "deactivated"} - if status not in legal_status: - raise ValueError(f"Illegal status value. Legal values: {legal_status}") - - data: openml._api_calls.DATA_TYPE = {"dataset_id": data_id, "status": status} - result = self._http.post("datasets/status/update", json=data).json() - server_data_id = result["dataset_id"] - server_status = result["status"] - if status != server_status or int(data_id) != int(server_data_id): - # This should never happen - raise ValueError("Data id/status does not collide") - - def _list_datasets( + def list( self, limit: int, offset: int, @@ -623,9 +496,6 @@ def _list_datasets( if value is not None: json[operator] = value - return self.__list_datasets(json=json) - - def __list_datasets(self, json: dict) -> pd.DataFrame: api_call = "datasets/list" datasets_list = self._http.post(api_call, json=json).json() # Minimalistic check if the JSON is useful @@ -654,6 +524,76 @@ def __list_datasets(self, json: dict) -> pd.DataFrame: } ) + def delete(self, dataset_id: int) -> bool: + raise NotImplementedError() + + def edit( # noqa: PLR0913 + self, + data_id: int, + description: str | None = None, + creator: str | None = None, + contributor: str | None = None, + collection_date: str | None = None, + language: str | None = None, + default_target_attribute: str | None = None, + ignore_attribute: str | list[str] | None = None, # type: ignore + citation: str | None = None, + row_id_attribute: str | None = None, + original_data_url: str | None = None, + paper_url: str | None = None, + ) -> int: + raise NotImplementedError() + + def fork(self, data_id: int) -> int: + raise NotImplementedError() + + def status_update(self, data_id: int, status: Literal["active", "deactivated"]) -> None: + """ + Updates the status of a dataset to either 'active' or 'deactivated'. + Please see the OpenML API documentation for a description of the status + and all legal status transitions: + https://docs.openml.org/concepts/data/#dataset-status + + Parameters + ---------- + data_id : int + The data id of the dataset + status : str, + 'active' or 'deactivated' + """ + legal_status = {"active", "deactivated"} + if status not in legal_status: + raise ValueError(f"Illegal status value. Legal values: {legal_status}") + + data: openml._api_calls.DATA_TYPE = {"dataset_id": data_id, "status": status} + result = self._http.post("datasets/status/update", json=data).json() + server_data_id = result["dataset_id"] + server_status = result["status"] + if status != server_status or int(data_id) != int(server_data_id): + # This should never happen + raise ValueError("Data id/status does not collide") + + def list_qualities(self) -> list[str]: # type: ignore + """Return list of data qualities available. + + The function performs an API call to retrieve the entire list of + data qualities that are computed on the datasets uploaded. + + Returns + ------- + list + """ + api_call = "datasets/qualities/list" + qualities = self._http.get(api_call).json() + # Minimalistic check if the XML is useful + if "data_qualities_list" not in qualities: + raise ValueError('Error in return XML, does not contain "oml:data_qualities_list"') + + if not isinstance(qualities["data_qualities_list"]["quality"], list): + raise TypeError('Error in return json, does not contain "quality" as a list') + + return qualities["data_qualities_list"]["quality"] + def _create_dataset_from_json(self, json_content: dict) -> OpenMLDataset: """Create a dataset given a json. @@ -702,3 +642,9 @@ def _create_dataset_from_json(self, json_content: dict) -> OpenMLDataset: parquet_url=json_content.get("parquet_url"), parquet_file=str(parquet_file) if parquet_file is not None else None, ) + + def feature_add_ontology(self, data_id: int, index: int, ontology: str) -> bool: + raise NotImplementedError() + + def feature_remove_ontology(self, data_id: int, index: int, ontology: str) -> bool: + raise NotImplementedError() From 9bcbcb32c232bb35b34e90ad7739de6c938ee5f3 Mon Sep 17 00:00:00 2001 From: JATAYU000 Date: Fri, 9 Jan 2026 13:01:34 +0530 Subject: [PATCH 09/10] Refactor functions, except get --- openml/datasets/functions.py | 181 ++++------------------------------- 1 file changed, 21 insertions(+), 160 deletions(-) diff --git a/openml/datasets/functions.py b/openml/datasets/functions.py index ac5466a44..23cdefdd2 100644 --- a/openml/datasets/functions.py +++ b/openml/datasets/functions.py @@ -5,7 +5,6 @@ import logging import os import warnings -from collections import OrderedDict from functools import partial from pathlib import Path from pyexpat import ExpatError @@ -22,6 +21,7 @@ import openml._api_calls import openml.utils +from openml._api import api_context from openml.config import OPENML_SKIP_PARQUET_ENV_VAR from openml.exceptions import ( OpenMLHashException, @@ -65,17 +65,7 @@ def list_qualities() -> list[str]: ------- list """ - api_call = "data/qualities/list" - xml_string = openml._api_calls._perform_api_call(api_call, "get") - qualities = xmltodict.parse(xml_string, force_list=("oml:quality")) - # Minimalistic check if the XML is useful - if "oml:data_qualities_list" not in qualities: - raise ValueError('Error in return XML, does not contain "oml:data_qualities_list"') - - if not isinstance(qualities["oml:data_qualities_list"]["oml:quality"], list): - raise TypeError('Error in return XML, does not contain "oml:quality" as a list') - - return qualities["oml:data_qualities_list"]["oml:quality"] + return api_context.backend.datasets.list_qualities() def list_datasets( @@ -129,7 +119,7 @@ def list_datasets( these are also included as columns. """ listing_call = partial( - _list_datasets, + api_context.backend.datasets.list, data_id=data_id, status=status, tag=tag, @@ -147,92 +137,6 @@ def list_datasets( return pd.concat(batches) -def _list_datasets( - limit: int, - offset: int, - *, - data_id: list[int] | None = None, - **kwargs: Any, -) -> pd.DataFrame: - """ - Perform api call to return a list of all datasets. - - Parameters - ---------- - The arguments that are lists are separated from the single value - ones which are put into the kwargs. - display_errors is also separated from the kwargs since it has a - default value. - - limit : int - The maximum number of datasets to show. - offset : int - The number of datasets to skip, starting from the first. - data_id : list, optional - - kwargs : dict, optional - Legal filter operators (keys in the dict): - tag, status, limit, offset, data_name, data_version, number_instances, - number_features, number_classes, number_missing_values. - - Returns - ------- - datasets : dataframe - """ - api_call = "data/list" - - if limit is not None: - api_call += f"/limit/{limit}" - if offset is not None: - api_call += f"/offset/{offset}" - - if kwargs is not None: - for operator, value in kwargs.items(): - if value is not None: - api_call += f"/{operator}/{value}" - if data_id is not None: - api_call += f"/data_id/{','.join([str(int(i)) for i in data_id])}" - return __list_datasets(api_call=api_call) - - -def __list_datasets(api_call: str) -> pd.DataFrame: - xml_string = openml._api_calls._perform_api_call(api_call, "get") - datasets_dict = xmltodict.parse(xml_string, force_list=("oml:dataset",)) - - # Minimalistic check if the XML is useful - assert isinstance(datasets_dict["oml:data"]["oml:dataset"], list), type( - datasets_dict["oml:data"], - ) - assert datasets_dict["oml:data"]["@xmlns:oml"] == "http://openml.org/openml", datasets_dict[ - "oml:data" - ]["@xmlns:oml"] - - datasets = {} - for dataset_ in datasets_dict["oml:data"]["oml:dataset"]: - ignore_attribute = ["oml:file_id", "oml:quality"] - dataset = { - k.replace("oml:", ""): v for (k, v) in dataset_.items() if k not in ignore_attribute - } - dataset["did"] = int(dataset["did"]) - dataset["version"] = int(dataset["version"]) - - # The number of qualities can range from 0 to infinity - for quality in dataset_.get("oml:quality", []): - try: - dataset[quality["@name"]] = int(quality["#text"]) - except ValueError: - dataset[quality["@name"]] = float(quality["#text"]) - datasets[dataset["did"]] = dataset - - return pd.DataFrame.from_dict(datasets, orient="index").astype( - { - "did": int, - "version": int, - "status": pd.CategoricalDtype(["active", "deactivated", "in_preparation"]), - } - ) - - def _expand_parameter(parameter: str | list[str] | None) -> list[str]: expanded_parameter = [] if isinstance(parameter, str): @@ -808,14 +712,7 @@ def status_update(data_id: int, status: Literal["active", "deactivated"]) -> Non if status not in legal_status: raise ValueError(f"Illegal status value. Legal values: {legal_status}") - data: openml._api_calls.DATA_TYPE = {"data_id": data_id, "status": status} - result_xml = openml._api_calls._perform_api_call("data/status/update", "post", data=data) - result = xmltodict.parse(result_xml) - server_data_id = result["oml:data_status_update"]["oml:id"] - server_status = result["oml:data_status_update"]["oml:status"] - if status != server_status or int(data_id) != int(server_data_id): - # This should never happen - raise ValueError("Data id/status does not collide") + api_context.backend.datasets.status_update(data_id=data_id, status=status) def edit_dataset( @@ -889,43 +786,20 @@ def edit_dataset( ------- Dataset id """ - if not isinstance(data_id, int): - raise TypeError(f"`data_id` must be of type `int`, not {type(data_id)}.") - - # compose data edit parameters as xml - form_data = {"data_id": data_id} # type: openml._api_calls.DATA_TYPE - xml = OrderedDict() # type: 'OrderedDict[str, OrderedDict]' - xml["oml:data_edit_parameters"] = OrderedDict() - xml["oml:data_edit_parameters"]["@xmlns:oml"] = "http://openml.org/openml" - xml["oml:data_edit_parameters"]["oml:description"] = description - xml["oml:data_edit_parameters"]["oml:creator"] = creator - xml["oml:data_edit_parameters"]["oml:contributor"] = contributor - xml["oml:data_edit_parameters"]["oml:collection_date"] = collection_date - xml["oml:data_edit_parameters"]["oml:language"] = language - xml["oml:data_edit_parameters"]["oml:default_target_attribute"] = default_target_attribute - xml["oml:data_edit_parameters"]["oml:row_id_attribute"] = row_id_attribute - xml["oml:data_edit_parameters"]["oml:ignore_attribute"] = ignore_attribute - xml["oml:data_edit_parameters"]["oml:citation"] = citation - xml["oml:data_edit_parameters"]["oml:original_data_url"] = original_data_url - xml["oml:data_edit_parameters"]["oml:paper_url"] = paper_url - - # delete None inputs - for k in list(xml["oml:data_edit_parameters"]): - if not xml["oml:data_edit_parameters"][k]: - del xml["oml:data_edit_parameters"][k] - - file_elements = { - "edit_parameters": ("description.xml", xmltodict.unparse(xml)), - } # type: openml._api_calls.FILE_ELEMENTS_TYPE - result_xml = openml._api_calls._perform_api_call( - "data/edit", - "post", - data=form_data, - file_elements=file_elements, + return api_context.backend.datasets.edit( + data_id, + description, + creator, + contributor, + collection_date, + language, + default_target_attribute, + ignore_attribute, + citation, + row_id_attribute, + original_data_url, + paper_url, ) - result = xmltodict.parse(result_xml) - data_id = result["oml:data_edit"]["oml:id"] - return int(data_id) def fork_dataset(data_id: int) -> int: @@ -957,14 +831,7 @@ def fork_dataset(data_id: int) -> int: Dataset id of the forked dataset """ - if not isinstance(data_id, int): - raise TypeError(f"`data_id` must be of type `int`, not {type(data_id)}.") - # compose data fork parameters - form_data = {"data_id": data_id} # type: openml._api_calls.DATA_TYPE - result_xml = openml._api_calls._perform_api_call("data/fork", "post", data=form_data) - result = xmltodict.parse(result_xml) - data_id = result["oml:data_fork"]["oml:id"] - return int(data_id) + return api_context.backend.datasets.fork(data_id=data_id) def data_feature_add_ontology(data_id: int, index: int, ontology: str) -> bool: @@ -988,10 +855,7 @@ def data_feature_add_ontology(data_id: int, index: int, ontology: str) -> bool: ------- True or throws an OpenML server exception """ - upload_data: dict[str, int | str] = {"data_id": data_id, "index": index, "ontology": ontology} - openml._api_calls._perform_api_call("data/feature/ontology/add", "post", data=upload_data) - # an error will be thrown in case the request was unsuccessful - return True + return api_context.backend.datasets.feature_add_ontology(data_id, index, ontology) def data_feature_remove_ontology(data_id: int, index: int, ontology: str) -> bool: @@ -1014,10 +878,7 @@ def data_feature_remove_ontology(data_id: int, index: int, ontology: str) -> boo ------- True or throws an OpenML server exception """ - upload_data: dict[str, int | str] = {"data_id": data_id, "index": index, "ontology": ontology} - openml._api_calls._perform_api_call("data/feature/ontology/remove", "post", data=upload_data) - # an error will be thrown in case the request was unsuccessful - return True + return api_context.backend.datasets.feature_remove_ontology(data_id, index, ontology) def _topic_add_dataset(data_id: int, topic: str) -> int: @@ -1460,4 +1321,4 @@ def delete_dataset(dataset_id: int) -> bool: bool True if the deletion was successful. False otherwise. """ - return openml.utils._delete_entity("data", dataset_id) + return api_context.backend.datasets.delete(dataset_id) From 96df5e30b46ea80633cb9593ceacf36ff10c8308 Mon Sep 17 00:00:00 2001 From: JATAYU000 Date: Fri, 9 Jan 2026 15:38:07 +0530 Subject: [PATCH 10/10] Remove circular import using lazy import --- openml/datasets/functions.py | 17 ++++++++++++++++- 1 file changed, 16 insertions(+), 1 deletion(-) diff --git a/openml/datasets/functions.py b/openml/datasets/functions.py index 23cdefdd2..6ede42ea9 100644 --- a/openml/datasets/functions.py +++ b/openml/datasets/functions.py @@ -21,7 +21,6 @@ import openml._api_calls import openml.utils -from openml._api import api_context from openml.config import OPENML_SKIP_PARQUET_ENV_VAR from openml.exceptions import ( OpenMLHashException, @@ -65,6 +64,8 @@ def list_qualities() -> list[str]: ------- list """ + from openml._api import api_context + return api_context.backend.datasets.list_qualities() @@ -118,6 +119,8 @@ def list_datasets( If qualities are calculated for the dataset, some of these are also included as columns. """ + from openml._api import api_context + listing_call = partial( api_context.backend.datasets.list, data_id=data_id, @@ -708,6 +711,8 @@ def status_update(data_id: int, status: Literal["active", "deactivated"]) -> Non status : str, 'active' or 'deactivated' """ + from openml._api import api_context + legal_status = {"active", "deactivated"} if status not in legal_status: raise ValueError(f"Illegal status value. Legal values: {legal_status}") @@ -786,6 +791,8 @@ def edit_dataset( ------- Dataset id """ + from openml._api import api_context + return api_context.backend.datasets.edit( data_id, description, @@ -831,6 +838,8 @@ def fork_dataset(data_id: int) -> int: Dataset id of the forked dataset """ + from openml._api import api_context + return api_context.backend.datasets.fork(data_id=data_id) @@ -855,6 +864,8 @@ def data_feature_add_ontology(data_id: int, index: int, ontology: str) -> bool: ------- True or throws an OpenML server exception """ + from openml._api import api_context + return api_context.backend.datasets.feature_add_ontology(data_id, index, ontology) @@ -878,6 +889,8 @@ def data_feature_remove_ontology(data_id: int, index: int, ontology: str) -> boo ------- True or throws an OpenML server exception """ + from openml._api import api_context + return api_context.backend.datasets.feature_remove_ontology(data_id, index, ontology) @@ -1321,4 +1334,6 @@ def delete_dataset(dataset_id: int) -> bool: bool True if the deletion was successful. False otherwise. """ + from openml._api import api_context + return api_context.backend.datasets.delete(dataset_id)