[ENH] V1 -> V2 Migration - Flows (module)

openml/_api/__init__.py

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+from openml._api.runtime.core import APIContext
+
+
+def set_api_version(version: str, *, strict: bool = False) -> None:
+    api_context.set_version(version=version, strict=strict)
+
+
+api_context = APIContext()

openml/_api/config.py

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+from __future__ import annotations
+
+from dataclasses import dataclass
+from typing import Literal
+
+DelayMethod = Literal["human", "robot"]
+
+
+@dataclass
+class APIConfig:
+    server: str
+    base_url: str
+    key: str
+    timeout: int = 10  # seconds
+
+
+@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(),
+)

openml/_api/http/__init__.py

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+from openml._api.http.client import HTTPClient
+
+__all__ = ["HTTPClient"]

openml/_api/http/client.py

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+from __future__ import annotations
+
+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 CacheMixin:
+    @property
+    def dir(self) -> str:
+        return settings.cache.dir
+
+    @property
+    def ttl(self) -> int:
+        return settings.cache.ttl
+
+    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("/")
+
+        # 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, cache_dir: Path) -> Response:  # noqa: ARG002
+        return Response()
+
+    def _set_cache_response(self, cache_dir: Path, 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__}"}
+
+    @property
+    def server(self) -> str:
+        return self.config.server
+
+    @property
+    def base_url(self) -> str:
+        return self.config.base_url
+
+    @property
+    def key(self) -> str:
+        return self.config.key
+
+    @property
+    def timeout(self) -> int:
+        return self.config.timeout
+
+    def request(
+        self,
+        method: str,
+        path: str,
+        *,
+        use_cache: bool = False,
+        use_api_key: bool = False,
+        **request_kwargs: Any,
+    ) -> Response:
+        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.key
+
+        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)
+
+        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(cache_dir, response)
+
+        return response
+
+    def get(
+        self,
+        path: str,
+        *,
+        use_cache: bool = False,
+        use_api_key: bool = False,
+        **request_kwargs: Any,
+    ) -> Response:
+        # 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,
+        **request_kwargs: Any,
+    ) -> Response:
+        return self.request(
+            method="DELETE",
+            path=path,
+            use_cache=False,
+            use_api_key=True,
+            **request_kwargs,
+        )

openml/_api/resources/__init__.py

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+from openml._api.resources.datasets import DatasetsV1, DatasetsV2
+from openml._api.resources.flows import FlowsV1, FlowsV2
+from openml._api.resources.tasks import TasksV1, TasksV2
+
+__all__ = [
+    "DatasetsV1",
+    "DatasetsV2",
+    "FlowsV1",
+    "FlowsV2",
+    "TasksV1",
+    "TasksV2",
+]

openml/_api/resources/base.py

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+from __future__ import annotations
+
+from abc import ABC, abstractmethod
+from typing import TYPE_CHECKING
+
+if TYPE_CHECKING:
+    import pandas as pd
+    from requests import Response
+
+    from openml._api.http import HTTPClient
+    from openml.datasets.dataset import OpenMLDataset
+    from openml.flows.flow import OpenMLFlow
+    from openml.tasks.task import OpenMLTask
+
+
+class ResourceAPI:
+    def __init__(self, http: HTTPClient):
+        self._http = http
+
+
+class DatasetsAPI(ResourceAPI, ABC):
+    @abstractmethod
+    def get(self, dataset_id: int) -> OpenMLDataset | tuple[OpenMLDataset, Response]: ...
+
+
+class TasksAPI(ResourceAPI, ABC):
+    @abstractmethod
+    def get(
+        self,
+        task_id: int,
+        *,
+        return_response: bool = False,
+    ) -> OpenMLTask | tuple[OpenMLTask, Response]: ...
+
+
+class FlowsAPI(ResourceAPI, ABC):
+    @abstractmethod
+    def get(
+        self,
+        flow_id: int,
+    ) -> OpenMLFlow: ...
+
+    @abstractmethod
+    def exists(self, name: str, external_version: str) -> int | bool: ...
+
+    @abstractmethod
+    def list(
+        self,
+        *,
+        limit: int | None = None,
+        offset: int | None = None,
+        tag: str | None = None,
+        uploader: str | None = None,
+    ) -> pd.DataFrame: ...
+
+    @abstractmethod
+    def create(self, flow: OpenMLFlow) -> OpenMLFlow | tuple[OpenMLFlow, Response]: ...
+
+    @abstractmethod
+    def delete(self, flow_id: int) -> None | Response: ...

openml/_api/resources/datasets.py

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+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, dataset_id: int) -> OpenMLDataset | tuple[OpenMLDataset, Response]:
+        raise NotImplementedError
+
+
+class DatasetsV2(DatasetsAPI):
+    def get(self, dataset_id: int) -> OpenMLDataset | tuple[OpenMLDataset, Response]:
+        raise NotImplementedError