optimize: complete _differentiable_functions (private)

scipy-stubs/optimize/_differentiable_functions.pyi

@@ -1,91 +1,229 @@
-from typing import Final
+from collections.abc import Callable, Sequence
+from types import ModuleType
+from typing import Any, Concatenate, Final, Generic, Literal, TypeAlias, overload
+from typing_extensions import TypeVar
 
-from scipy._typing import Untyped
+import numpy as np
+import optype.numpy as onp
+from scipy.sparse import csr_matrix, sparray, spmatrix
+from scipy.sparse.linalg import LinearOperator
+from ._hessian_update_strategy import HessianUpdateStrategy
 
-FD_METHODS: Final = ("2-point", "3-point", "cs")
+_XT = TypeVar("_XT", bound=np.floating[Any], default=np.floating[Any])
+_XT_contra = TypeVar("_XT_contra", bound=np.floating[Any], default=np.floating[Any], contravariant=True)
 
-class ScalarFunction:
-    xp: Untyped
-    x: Untyped
-    x_dtype: Untyped
-    n: Untyped
+_ToFloat64Vec: TypeAlias = Sequence[float | np.float64 | np.integer[Any] | np.bool_] | onp.CanArrayND[np.float64]
+_ToJac: TypeAlias = onp.ToFloat2D | spmatrix | sparray
+_ToHess: TypeAlias = _ToJac | LinearOperator
+
+_Vec: TypeAlias = onp.Array1D[_XT]
+_Jac: TypeAlias = onp.Array2D[_XT] | csr_matrix
+_Hess: TypeAlias = _Jac[_XT] | LinearOperator
+
+_ScalarFun: TypeAlias = Callable[Concatenate[onp.Array1D[_XT], ...], onp.ToFloat]
+_VectorFun: TypeAlias = Callable[Concatenate[onp.Array1D[_XT], ...], onp.ToFloat1D]
+_JacFun: TypeAlias = Callable[Concatenate[onp.Array1D[_XT], ...], _ToJac]
+_HessFun: TypeAlias = Callable[Concatenate[onp.Array1D[_XT], ...], _ToHess]
+
+_FDMethod: TypeAlias = Literal["2-point", "3-point", "cs"]
+_FDBounds: TypeAlias = onp.ToFloat1D | onp.ToFloat2D  # len-2 array-like of scalar- or vector-likes
+
+_ToGradFun: TypeAlias = _VectorFun[_XT_contra] | _FDMethod
+_ToJacFun: TypeAlias = _JacFun[_XT_contra] | _FDMethod
+_ToHessFun: TypeAlias = _HessFun[_XT_contra] | _FDMethod | HessianUpdateStrategy
+
+###
+
+FD_METHODS: Final = "2-point", "3-point", "cs"
+
+# TODO(jorenham): Array API compatibility
+# https://github.com/jorenham/scipy-stubs/issues/140
+
+class ScalarFunction(Generic[_XT_contra]):
+    xp: Final[ModuleType]
+
+    _args: Final[tuple[object, ...]]
+
+    n: Final[int]
+    x_dtype: _XT_contra  # readonly
+    x: _Vec[_XT_contra]
+    x_prev: _Vec[_XT_contra] | None
+    _lowest_x: _Vec[_XT_contra] | None
+
+    _orig_fun: _ScalarFun[_XT_contra]  # readonly
+    _wrapped_fun: Callable[[onp.Array1D[_XT_contra]], onp.ToFloat]  # readonly
+    _nfev: Final[list[int]]  # size 1
+    _lowest_f: onp.ToFloat
     f_updated: bool
+
+    _orig_grad: _ToGradFun[_XT_contra]  # readonly
+    _wrapped_grad: Callable[[onp.Array1D[_XT_contra]], _Vec]  # readonly
+    _ngev: Final[list[int]]  # size 1
+    g_prev: _Vec | None
     g_updated: bool
+
+    _orig_hess: _ToHessFun[_XT_contra]  # readonly
+    _wrapped_hess: Callable[[onp.Array1D[_XT_contra]], _Hess]  # readonly
+    _nhev: Final[list[int]]  # size 1
+    H: _Hess
     H_updated: bool
-    H: Untyped
-    x_prev: Untyped
-    g_prev: Untyped
-    def __init__(
-        self,
-        fun: Untyped,
-        x0: Untyped,
-        args: Untyped,
-        grad: Untyped,
-        hess: Untyped,
-        finite_diff_rel_step: Untyped,
-        finite_diff_bounds: Untyped,
-        epsilon: Untyped | None = None,
-    ) -> None: ...
+
+    #
     @property
-    def nfev(self) -> Untyped: ...
+    def nfev(self, /) -> int: ...
     @property
-    def ngev(self) -> Untyped: ...
+    def ngev(self, /) -> int: ...
     @property
-    def nhev(self) -> Untyped: ...
-    def fun(self, x: Untyped) -> Untyped: ...
-    def grad(self, x: Untyped) -> Untyped: ...
-    def hess(self, x: Untyped) -> Untyped: ...
-    def fun_and_grad(self, x: Untyped) -> Untyped: ...
-
-class VectorFunction:
-    xp: Untyped
-    x: Untyped
-    x_dtype: Untyped
-    n: Untyped
-    nfev: int
-    njev: int
-    nhev: int
+    def nhev(self, /) -> int: ...
+
+    #
+    @overload
+    def __init__(
+        self: ScalarFunction[np.float64],
+        /,
+        fun: _ScalarFun[np.float64],
+        x0: _ToFloat64Vec,
+        args: tuple[object, ...],
+        grad: _ToGradFun[np.float64],
+        hess: _ToHessFun[np.float64],
+        finite_diff_rel_step: onp.ToFloat | onp.ToFloat1D | None,
+        finite_diff_bounds: _FDBounds,
+        epsilon: onp.ToFloat | onp.ToFloat1D | None = None,
+    ) -> None: ...
+    @overload
+    def __init__(
+        self,
+        /,
+        fun: _ScalarFun[_XT_contra],
+        x0: _Vec[_XT_contra] | onp.ToFloat1D,
+        args: tuple[object, ...],
+        grad: _ToGradFun[_XT_contra],
+        hess: _ToHessFun[_XT_contra],
+        finite_diff_rel_step: onp.ToFloat | onp.ToFloat1D | None,
+        finite_diff_bounds: _FDBounds,
+        epsilon: onp.ToFloat | onp.ToFloat1D | None = None,
+    ) -> None: ...
+
+    #
+    def _update_x(self, /, x: onp.ToFloat1D) -> None: ...
+    def _update_fun(self, /) -> None: ...
+    def _update_grad(self, /) -> None: ...
+    def _update_hess(self, /) -> None: ...
+
+    #
+    def fun(self, /, x: onp.ToFloat1D) -> float | np.floating[Any]: ...
+    def grad(self, /, x: onp.ToFloat1D) -> _Vec: ...
+    def hess(self, /, x: onp.ToFloat1D) -> _Hess: ...
+    def fun_and_grad(self, /, x: onp.ToFloat1D) -> tuple[float | np.floating[Any], _Vec]: ...
+
+class VectorFunction(Generic[_XT_contra]):
+    xp: Final[ModuleType]
+
+    n: Final[int]
+    x_dtype: _XT_contra  # readonly
+    x: _Vec[_XT_contra]
+    x_diff: _Vec[_XT_contra]
+    x_prev: _Vec[_XT_contra] | None
+
+    m: Final[int]
+    f: _Vec
+    v: _Vec
     f_updated: bool
+    nfev: int
+
+    sparse_jacobian: Final[bool]
+    J: _Jac
+    J_prev: _Jac | None
     J_updated: bool
+    njev: int
+
+    H: _Hess
     H_updated: bool
-    x_diff: Untyped
-    f: Untyped
-    v: Untyped
-    m: Untyped
-    J: Untyped
-    sparse_jacobian: bool
-    H: Untyped
-    x_prev: Untyped
-    J_prev: Untyped
+    nhev: int
+
+    @overload
+    def __init__(
+        self: VectorFunction[np.float64],
+        /,
+        fun: _VectorFun[np.float64],
+        x0: _ToFloat64Vec,
+        jac: _ToJacFun[np.float64],
+        hess: _ToHessFun[np.float64],
+        finite_diff_rel_step: onp.ToFloat | onp.ToFloat1D | None,
+        finite_diff_jac_sparsity: _ToJac | None,
+        finite_diff_bounds: _FDBounds,
+        sparse_jacobian: onp.ToBool | None,
+    ) -> None: ...
+    @overload
     def __init__(
         self,
-        fun: Untyped,
-        x0: Untyped,
-        jac: Untyped,
-        hess: Untyped,
-        finite_diff_rel_step: Untyped,
-        finite_diff_jac_sparsity: Untyped,
-        finite_diff_bounds: Untyped,
-        sparse_jacobian: Untyped,
+        /,
+        fun: _VectorFun[_XT_contra],
+        x0: _Vec[_XT_contra] | onp.ToFloat1D,
+        jac: _ToJacFun[_XT_contra],
+        hess: _ToHessFun[_XT_contra],
+        finite_diff_rel_step: onp.ToFloat | onp.ToFloat1D | None,
+        finite_diff_jac_sparsity: _ToJac | None,
+        finite_diff_bounds: _FDBounds,
+        sparse_jacobian: onp.ToBool | None,
     ) -> None: ...
-    def fun(self, x: Untyped) -> Untyped: ...
-    def jac(self, x: Untyped) -> Untyped: ...
-    def hess(self, x: Untyped, v: Untyped) -> Untyped: ...
-
-class LinearVectorFunction:
-    J: Untyped
-    sparse_jacobian: bool
-    xp: Untyped
-    x: Untyped
-    x_dtype: Untyped
-    f: Untyped
+
+    #
+    def _update_v(self, /, v: onp.ToFloat1D) -> None: ...
+    def _update_x(self, /, x: onp.ToFloat1D) -> None: ...
+    def _update_fun(self, /) -> None: ...
+    def _update_jac(self, /) -> None: ...
+    def _update_hess(self, /) -> None: ...
+
+    #
+    def fun(self, /, x: onp.ToFloat1D) -> _Vec: ...
+    def jac(self, /, x: onp.ToFloat1D) -> _Jac: ...
+    def hess(self, /, x: onp.ToFloat1D, v: onp.ToFloat1D) -> _Hess: ...
+
+class LinearVectorFunction(Generic[_XT_contra]):
+    xp: Final[ModuleType]
+
+    n: Final[int]
+    x_dtype: _XT_contra  # readonly
+    x: _Vec[_XT_contra]
+
+    m: Final[int]
+    f: _Vec
     f_updated: bool
-    v: Untyped
-    H: Untyped
-    def __init__(self, A: Untyped, x0: Untyped, sparse_jacobian: Untyped) -> None: ...
-    def fun(self, x: Untyped) -> Untyped: ...
-    def jac(self, x: Untyped) -> Untyped: ...
-    def hess(self, x: Untyped, v: Untyped) -> Untyped: ...
-
-class IdentityVectorFunction(LinearVectorFunction):
-    def __init__(self, x0: Untyped, sparse_jacobian: Untyped) -> None: ...
+
+    sparse_jacobian: Final[bool]
+    J: Final[_Jac]
+
+    H: Final[csr_matrix]
+    v: _Vec[np.float64]
+
+    @overload
+    def __init__(
+        self: LinearVectorFunction[np.float64],
+        /,
+        A: onp.ToFloat2D | spmatrix | sparray,
+        x0: _ToFloat64Vec,
+        sparse_jacobian: onp.ToBool | None,
+    ) -> None: ...
+    @overload
+    def __init__(
+        self,
+        /,
+        A: onp.ToFloat2D | spmatrix | sparray,
+        x0: _Vec[_XT_contra] | onp.ToFloat1D,
+        sparse_jacobian: onp.ToBool | None,
+    ) -> None: ...
+
+    #
+    def _update_x(self, /, x: onp.ToFloat1D) -> None: ...
+
+    #
+    def fun(self, /, x: onp.ToFloat1D) -> _Vec: ...
+    def jac(self, /, x: onp.ToFloat1D) -> _Jac: ...
+    def hess(self, /, x: onp.ToFloat1D, v: _Vec[np.float64]) -> csr_matrix: ...
+
+class IdentityVectorFunction(LinearVectorFunction[_XT_contra]):
+    @overload
+    def __init__(self: IdentityVectorFunction[np.float64], /, x0: _ToFloat64Vec, sparse_jacobian: onp.ToBool | None) -> None: ...
+    @overload
+    def __init__(self, /, x0: onp.CanArrayND[_XT_contra] | onp.ToFloat1D, sparse_jacobian: onp.ToBool | None) -> None: ...