MAINT: integrate: break dependancy on optimize

Author: j-bowhay

scipy/_lib/_util.py

@@ -1189,6 +1189,51 @@ def __dir__(self):
         return list(self.keys())
 
 
+class OptimizeResult(_RichResult):
+    """
+    Represents the optimization result.
+
+    Attributes
+    ----------
+    x : ndarray
+        The solution of the optimization.
+    success : bool
+        Whether or not the optimizer exited successfully.
+    status : int
+        Termination status of the optimizer. Its value depends on the
+        underlying solver. Refer to `message` for details.
+    message : str
+        Description of the cause of the termination.
+    fun : float
+        Value of objective function at `x`.
+    jac, hess : ndarray
+        Values of objective function's Jacobian and its Hessian at `x` (if
+        available). The Hessian may be an approximation, see the documentation
+        of the function in question.
+    hess_inv : object
+        Inverse of the objective function's Hessian; may be an approximation.
+        Not available for all solvers. The type of this attribute may be
+        either np.ndarray or scipy.sparse.linalg.LinearOperator.
+    nfev, njev, nhev : int
+        Number of evaluations of the objective functions and of its
+        Jacobian and Hessian.
+    nit : int
+        Number of iterations performed by the optimizer.
+    maxcv : float
+        The maximum constraint violation.
+
+    Notes
+    -----
+    Depending on the specific solver being used, `OptimizeResult` may
+    not have all attributes listed here, and they may have additional
+    attributes not listed here. Since this class is essentially a
+    subclass of dict with attribute accessors, one can see which
+    attributes are available using the `OptimizeResult.keys` method.
+
+    """
+    pass
+
+
 def _indenter(s, n=0):
     """
     Ensures that lines after the first are indented by the specified amount

scipy/differentiate/tests/test_differentiate.py

@@ -4,6 +4,7 @@
 import numpy as np
 
 import scipy._lib._elementwise_iterative_method as eim
+from scipy._lib._util import OptimizeResult
 import scipy._lib.array_api_extra as xpx
 from scipy._lib._array_api_no_0d import xp_assert_close, xp_assert_equal, xp_assert_less
 from scipy._lib._array_api import is_numpy, is_torch
@@ -593,14 +594,14 @@ def df1_1xy(x, y):
         res01 = jacobian(lambda y: df1_0xy(z[0], y), z[1:2], initial_step=10)
         res10 = jacobian(lambda x: df1_1xy(x, z[1]), z[0:1], initial_step=10)
         res11 = jacobian(lambda y: df1_1xy(z[0], y), z[1:2], initial_step=10)
-        ref = optimize.OptimizeResult()
+        ref = OptimizeResult()
         for attr in ['success', 'status', 'df', 'nit', 'nfev']:
             ref_attr = xp.asarray([[getattr(res00, attr), getattr(res01, attr)],
                                    [getattr(res10, attr), getattr(res11, attr)]])
             ref[attr] = xp.squeeze(
                 ref_attr,
                 axis=tuple(ax for ax, size in enumerate(ref_attr.shape) if size == 1)
-            )            
+            )
             rtol = 1.5e-5 if res[attr].dtype == xp.float32 else 1.5e-14
             xp_assert_close(res[attr], ref[attr], rtol=rtol)
 

scipy/integrate/_bvp.py

@@ -6,7 +6,7 @@
 
 from scipy.sparse import coo_matrix, csc_matrix
 from scipy.sparse.linalg import splu
-from scipy.optimize import OptimizeResult
+from scipy._lib._util import OptimizeResult
 
 
 EPS = np.finfo(float).eps

scipy/integrate/_ivp/ivp.py

@@ -4,7 +4,7 @@
 from .radau import Radau
 from .rk import RK23, RK45, DOP853
 from .lsoda import LSODA
-from scipy.optimize import OptimizeResult
+from scipy._lib._util import OptimizeResult
 from .common import EPS, OdeSolution
 from .base import OdeSolver
 

scipy/optimize/_cobyla_py.py

@@ -14,8 +14,9 @@
 from threading import RLock
 
 import numpy as np
+from .._lib._util import OptimizeResult
 from scipy.optimize import _cobyla as cobyla
-from ._optimize import (OptimizeResult, _check_unknown_options,
+from ._optimize import (_check_unknown_options,
     _prepare_scalar_function)
 try:
     from itertools import izip

scipy/optimize/_differentialevolution.py

@@ -6,11 +6,11 @@
 
 import numpy as np
 
-from scipy.optimize import OptimizeResult, minimize
+from scipy.optimize import minimize
 from scipy.optimize._constraints import (Bounds, new_bounds_to_old,
                                          NonlinearConstraint, LinearConstraint)
 from scipy.optimize._optimize import _status_message, _wrap_callback
-from scipy._lib._util import (check_random_state, MapWrapper, _FunctionWrapper,
+from scipy._lib._util import (OptimizeResult, check_random_state, MapWrapper, _FunctionWrapper,
                               rng_integers, _transition_to_rng)
 from scipy._lib._sparse import issparse
 

scipy/optimize/_direct_py.py

@@ -3,7 +3,7 @@
 )
 
 import numpy as np
-from scipy.optimize import OptimizeResult
+from scipy._lib._util import OptimizeResult
 from ._constraints import old_bound_to_new, Bounds
 from ._direct import direct as _direct  # type: ignore
 

scipy/optimize/_dual_annealing.py

@@ -8,7 +8,7 @@
 """
 
 import numpy as np
-from scipy.optimize import OptimizeResult
+from scipy._lib._util import OptimizeResult
 from scipy.optimize import minimize, Bounds
 from scipy.special import gammaln
 from scipy._lib._util import check_random_state, _transition_to_rng

scipy/optimize/_isotonic.py

@@ -2,7 +2,7 @@
 
 import numpy as np
 
-from ._optimize import OptimizeResult
+from .._lib._util import OptimizeResult
 from ._pava_pybind import pava
 
 if TYPE_CHECKING:

scipy/optimize/_lbfgsb_py.py

@@ -35,8 +35,10 @@
 
 import numpy as np
 from numpy import array, asarray, float64, zeros
+
+from .._lib._util import OptimizeResult
 from . import _lbfgsb
-from ._optimize import (MemoizeJac, OptimizeResult, _call_callback_maybe_halt,
+from ._optimize import (MemoizeJac, _call_callback_maybe_halt,
                         _wrap_callback, _check_unknown_options,
                         _prepare_scalar_function)
 from ._constraints import old_bound_to_new

scipy/optimize/_linprog.py

@@ -16,7 +16,9 @@
 
 import numpy as np
 
-from ._optimize import OptimizeResult, OptimizeWarning
+from .._lib._util import OptimizeResult
+
+from ._optimize import OptimizeWarning
 from warnings import warn
 from ._linprog_highs import _linprog_highs
 from ._linprog_ip import _linprog_ip

scipy/optimize/_linprog_highs.py

@@ -15,7 +15,9 @@
 
 import inspect
 import numpy as np
-from ._optimize import OptimizeWarning, OptimizeResult
+
+from .._lib._util import OptimizeResult
+from ._optimize import OptimizeWarning
 from warnings import warn
 from ._highspy._highs_wrapper import _highs_wrapper
 from ._highspy._core import(

scipy/optimize/_linprog_ip.py

@@ -20,10 +20,11 @@
 
 import numpy as np
 import scipy as sp
+from .._lib._util import OptimizeResult
 import scipy.sparse as sps
 from warnings import warn
 from scipy.linalg import LinAlgError
-from ._optimize import OptimizeWarning, OptimizeResult, _check_unknown_options
+from ._optimize import OptimizeWarning, _check_unknown_options
 from ._linprog_util import _postsolve
 has_umfpack = True
 has_cholmod = True

scipy/optimize/_linprog_rs.py

@@ -25,7 +25,7 @@
 from ._bglu_dense import LU
 from ._bglu_dense import BGLU as BGLU
 from ._linprog_util import _postsolve
-from ._optimize import OptimizeResult
+from .._lib._util import OptimizeResult
 
 
 def _phase_one(A, b, x0, callback, postsolve_args, maxiter, tol, disp,

scipy/optimize/_linprog_simplex.py

@@ -30,7 +30,9 @@
 
 import numpy as np
 from warnings import warn
-from ._optimize import OptimizeResult, OptimizeWarning, _check_unknown_options
+
+from .._lib._util import OptimizeResult
+from ._optimize import OptimizeWarning, _check_unknown_options
 from ._linprog_util import _postsolve
 
 

scipy/optimize/_lsq/bvls.py

@@ -1,7 +1,7 @@
 """Bounded-variable least-squares algorithm."""
 import numpy as np
 from numpy.linalg import norm, lstsq
-from scipy.optimize import OptimizeResult
+from scipy._lib._util import OptimizeResult
 
 from .common import print_header_linear, print_iteration_linear
 

scipy/optimize/_lsq/dogbox.py

@@ -44,7 +44,7 @@
 from numpy.linalg import lstsq, norm
 
 from scipy.sparse.linalg import LinearOperator, aslinearoperator, lsmr
-from scipy.optimize import OptimizeResult
+from scipy._lib._util import OptimizeResult
 from scipy._lib._util import _call_callback_maybe_halt
 
 

scipy/optimize/_lsq/least_squares.py

@@ -5,11 +5,11 @@
 from numpy.linalg import norm
 
 from scipy.sparse.linalg import LinearOperator
-from scipy.optimize import _minpack, OptimizeResult
+from scipy.optimize import _minpack
 from scipy.optimize._numdiff import approx_derivative, group_columns
 from scipy.optimize._minimize import Bounds
 from scipy._lib._sparse import issparse
-from scipy._lib._util import _workers_wrapper
+from scipy._lib._util import OptimizeResult, _workers_wrapper
 
 from .trf import trf
 from .dogbox import dogbox

scipy/optimize/_lsq/lsq_linear.py

@@ -3,7 +3,7 @@
 from numpy.linalg import norm
 from scipy.sparse import issparse, csr_array
 from scipy.sparse.linalg import LinearOperator, lsmr
-from scipy.optimize import OptimizeResult
+from scipy._lib._util import OptimizeResult
 from scipy.optimize._minimize import Bounds
 
 from .common import in_bounds, compute_grad

scipy/optimize/_lsq/trf.py

@@ -97,7 +97,7 @@
 from numpy.linalg import norm
 from scipy.linalg import svd, qr
 from scipy.sparse.linalg import lsmr
-from scipy.optimize import OptimizeResult
+from scipy._lib._util import OptimizeResult
 
 from .common import (
     step_size_to_bound, find_active_constraints, in_bounds,

scipy/optimize/_lsq/trf_linear.py

@@ -4,7 +4,7 @@
 from numpy.linalg import norm
 from scipy.linalg import qr, solve_triangular
 from scipy.sparse.linalg import lsmr
-from scipy.optimize import OptimizeResult
+from scipy._lib._util import OptimizeResult
 
 from .givens_elimination import givens_elimination
 from .common import (

scipy/optimize/_milp.py

@@ -6,7 +6,7 @@
 from scipy.sparse import csc_array, vstack, issparse
 from ._highspy._highs_wrapper import _highs_wrapper  # type: ignore[import-not-found,import-untyped]
 from ._constraints import LinearConstraint, Bounds
-from ._optimize import OptimizeResult
+from .._lib._util import OptimizeResult
 from ._linprog_highs import _highs_to_scipy_status_message
 
 

scipy/optimize/_minimize.py

@@ -15,12 +15,13 @@
 
 import numpy as np
 
+from .._lib._util import OptimizeResult
+
 # unconstrained minimization
 from ._optimize import (_minimize_neldermead, _minimize_powell, _minimize_cg,
                         _minimize_bfgs, _minimize_newtoncg,
                         _minimize_scalar_brent, _minimize_scalar_bounded,
-                        _minimize_scalar_golden, MemoizeJac, OptimizeResult,
-                        _wrap_callback, _recover_from_bracket_error)
+                        _minimize_scalar_golden, MemoizeJac, _wrap_callback, _recover_from_bracket_error)
 from ._trustregion_dogleg import _minimize_dogleg
 from ._trustregion_ncg import _minimize_trust_ncg
 from ._trustregion_krylov import _minimize_trust_krylov

scipy/optimize/_minpack_py.py

@@ -7,9 +7,9 @@
                    finfo, inexact, issubdtype, dtype)
 from scipy import linalg
 from scipy.linalg import svd, cholesky, solve_triangular, LinAlgError
-from scipy._lib._util import _asarray_validated, _lazywhere, _contains_nan
+from scipy._lib._util import OptimizeResult, _asarray_validated, _lazywhere, _contains_nan
 from scipy._lib._util import getfullargspec_no_self as _getfullargspec
-from ._optimize import OptimizeResult, _check_unknown_options, OptimizeWarning
+from ._optimize import _check_unknown_options, OptimizeWarning
 from ._lsq import least_squares
 # from ._lsq.common import make_strictly_feasible
 from ._lsq.least_squares import prepare_bounds

scipy/optimize/_optimize.py

@@ -31,15 +31,15 @@
 import inspect
 from numpy import eye, argmin, zeros, shape, asarray, sqrt
 import numpy as np
+from scipy._lib._util import OptimizeResult
 from scipy.linalg import cholesky, issymmetric, LinAlgError
 from scipy.sparse.linalg import LinearOperator
 from ._linesearch import (line_search_wolfe1, line_search_wolfe2,
                           line_search_wolfe2 as line_search,
                           LineSearchWarning)
 from ._numdiff import approx_derivative
 from scipy._lib._util import getfullargspec_no_self as _getfullargspec
-from scipy._lib._util import (MapWrapper, check_random_state, _RichResult,
-                              _call_callback_maybe_halt, _transition_to_rng)
+from scipy._lib._util import (MapWrapper, check_random_state, _call_callback_maybe_halt, _transition_to_rng)
 from scipy.optimize._differentiable_functions import ScalarFunction, FD_METHODS
 from scipy._lib._array_api import array_namespace, xp_capabilities
 from scipy._lib import array_api_extra as xpx
@@ -109,51 +109,6 @@ def wrapped_callback(res):
     return wrapped_callback
 
 
-class OptimizeResult(_RichResult):
-    """
-    Represents the optimization result.
-
-    Attributes
-    ----------
-    x : ndarray
-        The solution of the optimization.
-    success : bool
-        Whether or not the optimizer exited successfully.
-    status : int
-        Termination status of the optimizer. Its value depends on the
-        underlying solver. Refer to `message` for details.
-    message : str
-        Description of the cause of the termination.
-    fun : float
-        Value of objective function at `x`.
-    jac, hess : ndarray
-        Values of objective function's Jacobian and its Hessian at `x` (if
-        available). The Hessian may be an approximation, see the documentation
-        of the function in question.
-    hess_inv : object
-        Inverse of the objective function's Hessian; may be an approximation.
-        Not available for all solvers. The type of this attribute may be
-        either np.ndarray or scipy.sparse.linalg.LinearOperator.
-    nfev, njev, nhev : int
-        Number of evaluations of the objective functions and of its
-        Jacobian and Hessian.
-    nit : int
-        Number of iterations performed by the optimizer.
-    maxcv : float
-        The maximum constraint violation.
-
-    Notes
-    -----
-    Depending on the specific solver being used, `OptimizeResult` may
-    not have all attributes listed here, and they may have additional
-    attributes not listed here. Since this class is essentially a
-    subclass of dict with attribute accessors, one can see which
-    attributes are available using the `OptimizeResult.keys` method.
-
-    """
-    pass
-
-
 class OptimizeWarning(UserWarning):
     """General warning for :mod:`scipy.optimize`."""
     pass

scipy/optimize/_qap.py

@@ -2,10 +2,10 @@
 import operator
 import warnings
 import numbers
-from . import (linear_sum_assignment, OptimizeResult)
+from . import (linear_sum_assignment)
 from ._optimize import _check_unknown_options
 
-from scipy._lib._util import check_random_state
+from scipy._lib._util import OptimizeResult, check_random_state
 import itertools
 
 QUADRATIC_ASSIGNMENT_METHODS = ['faq', '2opt']