Support typing #16705
Description
Describe the workflow you want to enable
Support Python typing, which is available in Python 3.5+, compatible with scikit-learn. This can enable tools to automatically get the estimator arg types (e.g., linters), for example. And it's a Python standard now.
Describe your proposed solution
For example, SVC could be like this:
def __init__(
self,
C: float = 1.0,
kernel: Union[Callable[[np.ndarray, np.ndarray], np.ndarray],
Literal["linear", "poly", "rbf", "sigmoid", "precomputed"]] = "rbf",
degree: int = 3,
gamma: Union[float, Literal["scale", "auto"]] = "scale",
coef0: float = 0.0,
shrinking: bool = True,
probability: bool = False,
tol: float = 1e-3,
cache_size: int = 200,
class_weight: Optional[Union[Mapping[int, float], Literal["balanced"]]] = None,
verbose: bool = False,
max_iter: int = -1,
decision_function_shape: Literal["ovo", "ovr"] = "ovr",
break_ties: bool = False,
random_state: Optional[Union[int, RandomState]] = None,
) -> None:Describe alternatives you've considered, if relevant
An alternative is to keep the types just in the docstrings. However, is hard for tools to parse the arg types.