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========================================================================================= FAILURES ========================================================================================== ________________________________________________________________________ test_scipy_method[trust-constr-True-False] _________________________________________________________________________ array_call = False, fixed = True, method = 'trust-constr' @pytest.mark.parametrize("array_call", (False, True)) @pytest.mark.parametrize("fixed", (False, True)) @pytest.mark.parametrize( "method", ( "Nelder-Mead", "Powell", "CG", "BFGS", "Newton-CG", "L-BFGS-B", "TNC", "COBYLA", "SLSQP", "trust-constr", "dogleg", "trust-ncg", "trust-exact", "trust-krylov", ), ) def test_scipy_method(array_call, fixed, method): fn = (lambda par: fcn(*par)) if array_call else fcn gr = None he = None hep = None if method in ( "Newton-CG", "trust-constr", "dogleg", "trust-ncg", "trust-exact", "trust-krylov", ): gr = (lambda par: grad(*par)) if array_call else grad if method in ("Newton-CG", "dogleg", "trust-ncg", "trust-exact", "trust-krylov"): he = (lambda par: hess(*par)) if array_call else hess if method in ("Newton-CG", "trust-ncg", "trust-krylov", "trust-constr"): hep = (lambda par, v: hessp(*par, v)) if array_call else hessp if array_call: m = Minuit(fn, (1, 2), grad=gr) else: m = Minuit(fn, a=1, b=2, grad=gr) m.fixed[0] = fixed > m.scipy(method=method, hess=he) tests/test_scipy.py:74: _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ ../stage/usr/local/lib/python3.11/site-packages/iminuit/minuit.py:1245: in scipy r = minimize( /usr/local/lib/python3.11/site-packages/scipy/optimize/_minimize.py:722: in minimize res = _minimize_trustregion_constr(fun, x0, args, jac, hess, hessp, /usr/local/lib/python3.11/site-packages/scipy/optimize/_trustregion_constr/minimize_trustregion_constr.py:514: in _minimize_trustregion_constr _, result = equality_constrained_sqp( /usr/local/lib/python3.11/site-packages/scipy/optimize/_trustregion_constr/equality_constrained_sqp.py:80: in equality_constrained_sqp Z, LS, Y = projections(A, factorization_method) /usr/local/lib/python3.11/site-packages/scipy/optimize/_trustregion_constr/projections.py:393: in projections = augmented_system_projections(A, m, n, orth_tol, max_refin, tol) /usr/local/lib/python3.11/site-packages/scipy/optimize/_trustregion_constr/projections.py:101: in augmented_system_projections solve = scipy.sparse.linalg.factorized(K) /usr/local/lib/python3.11/site-packages/scipy/sparse/linalg/_dsolve/linsolve.py:565: in factorized umf.numeric(A) /usr/local/lib/python3.11/site-packages/scikits/umfpack/umfpack.py:555: in numeric self.symbolic(mtx) _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ self = <scikits.umfpack.umfpack.UmfpackContext object at 0x2cb6dd94d2d0> mtx = <1x1 sparse matrix of type '<class 'numpy.float64'>' with 1 stored elements in Compressed Sparse Column format> def symbolic(self, mtx): """ Perform symbolic object (symbolic LU decomposition) computation for a given sparsity pattern. """ self.free_symbolic() indx = self._getIndx(mtx) if not assumeSortedIndices: # row/column indices cannot be assumed to be sorted mtx.sort_indices() if self.isReal: status, self._symbolic\ > = self.funs.symbolic(mtx.shape[0], mtx.shape[1], mtx.indptr, indx, mtx.data, self.control, self.info) E TypeError: umfpack_dl_symbolic() missing 1 required positional argument: 'Info' /usr/local/lib/python3.11/site-packages/scikits/umfpack/umfpack.py:522: TypeError ________________________________________________________________________ test_scipy_method[trust-constr-False-True] _________________________________________________________________________ array_call = True, fixed = False, method = 'trust-constr' @pytest.mark.parametrize("array_call", (False, True)) @pytest.mark.parametrize("fixed", (False, True)) @pytest.mark.parametrize( "method", ( "Nelder-Mead", "Powell", "CG", "BFGS", "Newton-CG", "L-BFGS-B", "TNC", "COBYLA", "SLSQP", "trust-constr", "dogleg", "trust-ncg", "trust-exact", "trust-krylov", ), ) def test_scipy_method(array_call, fixed, method): fn = (lambda par: fcn(*par)) if array_call else fcn gr = None he = None hep = None if method in ( "Newton-CG", "trust-constr", "dogleg", "trust-ncg", "trust-exact", "trust-krylov", ): gr = (lambda par: grad(*par)) if array_call else grad if method in ("Newton-CG", "dogleg", "trust-ncg", "trust-exact", "trust-krylov"): he = (lambda par: hess(*par)) if array_call else hess if method in ("Newton-CG", "trust-ncg", "trust-krylov", "trust-constr"): hep = (lambda par, v: hessp(*par, v)) if array_call else hessp if array_call: m = Minuit(fn, (1, 2), grad=gr) else: m = Minuit(fn, a=1, b=2, grad=gr) m.fixed[0] = fixed > m.scipy(method=method, hess=he) tests/test_scipy.py:74: _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ ../stage/usr/local/lib/python3.11/site-packages/iminuit/minuit.py:1245: in scipy r = minimize( /usr/local/lib/python3.11/site-packages/scipy/optimize/_minimize.py:722: in minimize res = _minimize_trustregion_constr(fun, x0, args, jac, hess, hessp, /usr/local/lib/python3.11/site-packages/scipy/optimize/_trustregion_constr/minimize_trustregion_constr.py:514: in _minimize_trustregion_constr _, result = equality_constrained_sqp( /usr/local/lib/python3.11/site-packages/scipy/optimize/_trustregion_constr/equality_constrained_sqp.py:80: in equality_constrained_sqp Z, LS, Y = projections(A, factorization_method) /usr/local/lib/python3.11/site-packages/scipy/optimize/_trustregion_constr/projections.py:393: in projections = augmented_system_projections(A, m, n, orth_tol, max_refin, tol) /usr/local/lib/python3.11/site-packages/scipy/optimize/_trustregion_constr/projections.py:101: in augmented_system_projections solve = scipy.sparse.linalg.factorized(K) /usr/local/lib/python3.11/site-packages/scipy/sparse/linalg/_dsolve/linsolve.py:565: in factorized umf.numeric(A) /usr/local/lib/python3.11/site-packages/scikits/umfpack/umfpack.py:555: in numeric self.symbolic(mtx) _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ self = <scikits.umfpack.umfpack.UmfpackContext object at 0x2cb6dd792f90> mtx = <2x2 sparse matrix of type '<class 'numpy.float64'>' with 2 stored elements in Compressed Sparse Column format> def symbolic(self, mtx): """ Perform symbolic object (symbolic LU decomposition) computation for a given sparsity pattern. """ self.free_symbolic() indx = self._getIndx(mtx) if not assumeSortedIndices: # row/column indices cannot be assumed to be sorted mtx.sort_indices() if self.isReal: status, self._symbolic\ > = self.funs.symbolic(mtx.shape[0], mtx.shape[1], mtx.indptr, indx, mtx.data, self.control, self.info) E TypeError: umfpack_dl_symbolic() missing 1 required positional argument: 'Info' /usr/local/lib/python3.11/site-packages/scikits/umfpack/umfpack.py:522: TypeError _________________________________________________________________________ test_scipy_method[trust-constr-True-True] _________________________________________________________________________ array_call = True, fixed = True, method = 'trust-constr' @pytest.mark.parametrize("array_call", (False, True)) @pytest.mark.parametrize("fixed", (False, True)) @pytest.mark.parametrize( "method", ( "Nelder-Mead", "Powell", "CG", "BFGS", "Newton-CG", "L-BFGS-B", "TNC", "COBYLA", "SLSQP", "trust-constr", "dogleg", "trust-ncg", "trust-exact", "trust-krylov", ), ) def test_scipy_method(array_call, fixed, method): fn = (lambda par: fcn(*par)) if array_call else fcn gr = None he = None hep = None if method in ( "Newton-CG", "trust-constr", "dogleg", "trust-ncg", "trust-exact", "trust-krylov", ): gr = (lambda par: grad(*par)) if array_call else grad if method in ("Newton-CG", "dogleg", "trust-ncg", "trust-exact", "trust-krylov"): he = (lambda par: hess(*par)) if array_call else hess if method in ("Newton-CG", "trust-ncg", "trust-krylov", "trust-constr"): hep = (lambda par, v: hessp(*par, v)) if array_call else hessp if array_call: m = Minuit(fn, (1, 2), grad=gr) else: m = Minuit(fn, a=1, b=2, grad=gr) m.fixed[0] = fixed > m.scipy(method=method, hess=he) tests/test_scipy.py:74: _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ ../stage/usr/local/lib/python3.11/site-packages/iminuit/minuit.py:1245: in scipy r = minimize( /usr/local/lib/python3.11/site-packages/scipy/optimize/_minimize.py:722: in minimize res = _minimize_trustregion_constr(fun, x0, args, jac, hess, hessp, /usr/local/lib/python3.11/site-packages/scipy/optimize/_trustregion_constr/minimize_trustregion_constr.py:514: in _minimize_trustregion_constr _, result = equality_constrained_sqp( /usr/local/lib/python3.11/site-packages/scipy/optimize/_trustregion_constr/equality_constrained_sqp.py:80: in equality_constrained_sqp Z, LS, Y = projections(A, factorization_method) /usr/local/lib/python3.11/site-packages/scipy/optimize/_trustregion_constr/projections.py:393: in projections = augmented_system_projections(A, m, n, orth_tol, max_refin, tol) /usr/local/lib/python3.11/site-packages/scipy/optimize/_trustregion_constr/projections.py:101: in augmented_system_projections solve = scipy.sparse.linalg.factorized(K) /usr/local/lib/python3.11/site-packages/scipy/sparse/linalg/_dsolve/linsolve.py:565: in factorized umf.numeric(A) /usr/local/lib/python3.11/site-packages/scikits/umfpack/umfpack.py:555: in numeric self.symbolic(mtx) _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ self = <scikits.umfpack.umfpack.UmfpackContext object at 0x2cb6dd5c96d0> mtx = <1x1 sparse matrix of type '<class 'numpy.float64'>' with 1 stored elements in Compressed Sparse Column format> def symbolic(self, mtx): """ Perform symbolic object (symbolic LU decomposition) computation for a given sparsity pattern. """ self.free_symbolic() indx = self._getIndx(mtx) if not assumeSortedIndices: # row/column indices cannot be assumed to be sorted mtx.sort_indices() if self.isReal: status, self._symbolic\ > = self.funs.symbolic(mtx.shape[0], mtx.shape[1], mtx.indptr, indx, mtx.data, self.control, self.info) E TypeError: umfpack_dl_symbolic() missing 1 required positional argument: 'Info' /usr/local/lib/python3.11/site-packages/scikits/umfpack/umfpack.py:522: TypeError ________________________________________________________________________ test_scipy_method[trust-constr-False-False] ________________________________________________________________________ array_call = False, fixed = False, method = 'trust-constr' @pytest.mark.parametrize("array_call", (False, True)) @pytest.mark.parametrize("fixed", (False, True)) @pytest.mark.parametrize( "method", ( "Nelder-Mead", "Powell", "CG", "BFGS", "Newton-CG", "L-BFGS-B", "TNC", "COBYLA", "SLSQP", "trust-constr", "dogleg", "trust-ncg", "trust-exact", "trust-krylov", ), ) def test_scipy_method(array_call, fixed, method): fn = (lambda par: fcn(*par)) if array_call else fcn gr = None he = None hep = None if method in ( "Newton-CG", "trust-constr", "dogleg", "trust-ncg", "trust-exact", "trust-krylov", ): gr = (lambda par: grad(*par)) if array_call else grad if method in ("Newton-CG", "dogleg", "trust-ncg", "trust-exact", "trust-krylov"): he = (lambda par: hess(*par)) if array_call else hess if method in ("Newton-CG", "trust-ncg", "trust-krylov", "trust-constr"): hep = (lambda par, v: hessp(*par, v)) if array_call else hessp if array_call: m = Minuit(fn, (1, 2), grad=gr) else: m = Minuit(fn, a=1, b=2, grad=gr) m.fixed[0] = fixed > m.scipy(method=method, hess=he) tests/test_scipy.py:74: _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ ../stage/usr/local/lib/python3.11/site-packages/iminuit/minuit.py:1245: in scipy r = minimize( /usr/local/lib/python3.11/site-packages/scipy/optimize/_minimize.py:722: in minimize res = _minimize_trustregion_constr(fun, x0, args, jac, hess, hessp, /usr/local/lib/python3.11/site-packages/scipy/optimize/_trustregion_constr/minimize_trustregion_constr.py:514: in _minimize_trustregion_constr _, result = equality_constrained_sqp( /usr/local/lib/python3.11/site-packages/scipy/optimize/_trustregion_constr/equality_constrained_sqp.py:80: in equality_constrained_sqp Z, LS, Y = projections(A, factorization_method) /usr/local/lib/python3.11/site-packages/scipy/optimize/_trustregion_constr/projections.py:393: in projections = augmented_system_projections(A, m, n, orth_tol, max_refin, tol) /usr/local/lib/python3.11/site-packages/scipy/optimize/_trustregion_constr/projections.py:101: in augmented_system_projections solve = scipy.sparse.linalg.factorized(K) /usr/local/lib/python3.11/site-packages/scipy/sparse/linalg/_dsolve/linsolve.py:565: in factorized umf.numeric(A) /usr/local/lib/python3.11/site-packages/scikits/umfpack/umfpack.py:555: in numeric self.symbolic(mtx) _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ self = <scikits.umfpack.umfpack.UmfpackContext object at 0x2cb6dd80a910> mtx = <2x2 sparse matrix of type '<class 'numpy.float64'>' with 2 stored elements in Compressed Sparse Column format> def symbolic(self, mtx): """ Perform symbolic object (symbolic LU decomposition) computation for a given sparsity pattern. """ self.free_symbolic() indx = self._getIndx(mtx) if not assumeSortedIndices: # row/column indices cannot be assumed to be sorted mtx.sort_indices() if self.isReal: status, self._symbolic\ > = self.funs.symbolic(mtx.shape[0], mtx.shape[1], mtx.indptr, indx, mtx.data, self.control, self.info) E TypeError: umfpack_dl_symbolic() missing 1 required positional argument: 'Info' /usr/local/lib/python3.11/site-packages/scikits/umfpack/umfpack.py:522: TypeError ============================================================================== 4 failed, 674 passed in 53.97s =============================================================================== *** Error code 1
Version: 2.30.1 Python-3.11 FreeBSD 14.1
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Version: 2.30.1
Python-3.11
FreeBSD 14.1
The text was updated successfully, but these errors were encountered: