diff --git a/.github/workflows/pytest.yml b/.github/workflows/pytest.yml index 63efd763..5b529458 100644 --- a/.github/workflows/pytest.yml +++ b/.github/workflows/pytest.yml @@ -42,10 +42,5 @@ jobs: uv pip install pytest pytest-cov pytest-benchmark hypothesis --system uv pip freeze --system - - name: Run tests with -Werror - if: matrix.python-version != '3.14' + - name: Run tests run: pytest --cov=pyerrors -vv -Werror - - - name: Run tests without -Werror for python 3.14 - if: matrix.python-version == '3.14' - run: pytest --cov=pyerrors -vv diff --git a/pyerrors/fits.py b/pyerrors/fits.py index 928634d8..3d41e581 100644 --- a/pyerrors/fits.py +++ b/pyerrors/fits.py @@ -481,7 +481,7 @@ def prepare_hat_matrix(): try: hess = hessian(chisqfunc)(fitp) - except TypeError: + except (TypeError, ValueError, np.linalg.LinAlgError): raise Exception("It is required to use autograd.numpy instead of numpy within fit functions, see the documentation for details.") from None len_y = len(y_f) @@ -722,7 +722,7 @@ def odr_chisquare(p): fitp = out.beta try: hess = hessian(odr_chisquare)(np.concatenate((fitp, out.xplusd.ravel()))) - except TypeError: + except (TypeError, ValueError, np.linalg.LinAlgError): raise Exception("It is required to use autograd.numpy instead of numpy within fit functions, see the documentation for details.") from None def odr_chisquare_compact_x(d): diff --git a/pyerrors/roots.py b/pyerrors/roots.py index acc5614f..dfce2bc0 100644 --- a/pyerrors/roots.py +++ b/pyerrors/roots.py @@ -32,10 +32,10 @@ def root_func(x, d): root = scipy.optimize.fsolve(func, guess, d_val) # Error propagation as detailed in arXiv:1809.01289 - dx = jacobian(func)(root[0], d_val) try: + dx = jacobian(func)(root[0], d_val) da = jacobian(lambda u, v: func(v, u))(d_val, root[0]) - except TypeError: + except (TypeError, ValueError, np.linalg.LinAlgError): raise Exception("It is required to use autograd.numpy instead of numpy within root functions, see the documentation for details.") from None deriv = - da / dx res = derived_observable(lambda x, **kwargs: (x[0] + np.finfo(np.float64).eps) / (np.array(d).reshape(-1)[0].value + np.finfo(np.float64).eps) * root[0], diff --git a/tests/fits_test.py b/tests/fits_test.py index 264947ef..772b668c 100644 --- a/tests/fits_test.py +++ b/tests/fits_test.py @@ -698,13 +698,27 @@ def func(a, x): y = a[0] * np.exp(-a[1] * x) return y - with pytest.raises(Exception): - pe.least_squares(x, oy, func) + def func_autograd(a, x): + return a[0] * anp.exp(-a[1] * x) + + # Since autograd 1.9.0 plain numpy ufuncs are dispatched to the autograd + # wrappers (ArrayBox.__array_ufunc__), so a function using numpy.exp now + # yields exactly the same result as one using autograd.numpy.exp. + for r_np, r_ag in zip(pe.least_squares(x, oy, func), pe.least_squares(x, oy, func_autograd)): + assert r_np == r_ag + for r_np, r_ag in zip(pe.total_least_squares(oy, oy, func), pe.total_least_squares(oy, oy, func_autograd)): + assert r_np == r_ag + + # A function that genuinely cannot be traced by autograd must still raise a + # clear error pointing the user to autograd.numpy. + def func_invalid(a, x): + return np.array(a[0] * np.exp(-a[1] * x), dtype=np.float64) - pe.least_squares(x, oy, func, num_grad=True) + with pytest.raises(Exception): + pe.least_squares(x, oy, func_invalid) with pytest.raises(Exception): - pe.total_least_squares(oy, oy, func) + pe.total_least_squares(oy, oy, func_invalid) def test_invalid_fit_function(): @@ -818,7 +832,14 @@ def func_a(a,x): def func_b(a,x): return a[0]*np.exp(a[2]*x) + def func_a_autograd(a,x): + return a[0]*anp.exp(a[1]*x) + + def func_b_autograd(a,x): + return a[0]*anp.exp(a[2]*x) + funcs = {'a':func_a, 'b':func_b} + funcs_autograd = {'a':func_a_autograd, 'b':func_b_autograd} xs = {'a':xvals_a, 'b':xvals_b} ys = {'a':[pe.Obs([np.random.normal(item, item*1.5, 1000)],['ensemble1']) for item in func_exp1(xvals_a)], 'b':[pe.Obs([np.random.normal(item, item*1.4, 1000)],['ensemble1']) for item in func_exp2(xvals_b)]} @@ -826,8 +847,17 @@ def func_b(a,x): for key in funcs.keys(): [item.gamma_method() for item in ys[key]] + # Since autograd 1.9.0 plain numpy ufuncs are dispatched to the autograd + # wrappers, so the fit using numpy now matches the one using autograd.numpy. + for r_np, r_ag in zip(pe.least_squares(xs, ys, funcs), pe.least_squares(xs, ys, funcs_autograd)): + assert r_np == r_ag + + # A function that genuinely cannot be traced by autograd must still raise. + def func_a_invalid(a, x): + return np.array(a[0] * np.exp(a[1] * x), dtype=np.float64) + with pytest.raises(Exception): - pe.least_squares(xs, ys, funcs) + pe.least_squares(xs, ys, {'a': func_a_invalid, 'b': func_b}) pe.least_squares(xs, ys, funcs, num_grad=True) @@ -930,7 +960,14 @@ def func_a(a,x): def func_b(a,x): return a[0]*np.exp(a[2]*x) + def func_a_autograd(a,x): + return a[0]*anp.exp(a[1]*x) + + def func_b_autograd(a,x): + return a[0]*anp.exp(a[2]*x) + funcs = {'a':func_a, 'b':func_b} + funcs_autograd = {'a':func_a_autograd, 'b':func_b_autograd} xs = {'a':xvals_a, 'b':xvals_b} ys = {'a':[pe.Obs([np.random.normal(item, item*1.5, 1000)],['ensemble1']) for item in func_exp1(xvals_a)], 'b':[pe.Obs([np.random.normal(item, item*1.4, 1000)],['ensemble1']) for item in func_exp2(xvals_b)]} @@ -938,8 +975,17 @@ def func_b(a,x): for key in funcs.keys(): [item.gamma_method() for item in ys[key]] + # Since autograd 1.9.0 plain numpy ufuncs are dispatched to the autograd + # wrappers, so the fit using numpy now matches the one using autograd.numpy. + for r_np, r_ag in zip(pe.least_squares(xs, ys, funcs), pe.least_squares(xs, ys, funcs_autograd)): + assert r_np == r_ag + + # A function that genuinely cannot be traced by autograd must still raise. + def func_a_invalid(a, x): + return np.array(a[0] * np.exp(a[1] * x), dtype=np.float64) + with pytest.raises(Exception): - pe.least_squares(xs, ys, funcs) + pe.least_squares(xs, ys, {'a': func_a_invalid, 'b': func_b}) pe.least_squares(xs, ys, funcs, num_grad=True) diff --git a/tests/roots_test.py b/tests/roots_test.py index 89feba96..83f2393c 100644 --- a/tests/roots_test.py +++ b/tests/roots_test.py @@ -1,4 +1,5 @@ import numpy as np +import autograd.numpy as anp import pyerrors as pe import pytest @@ -37,11 +38,23 @@ def test_root_no_autograd(): def root_function(x, d): return x - np.log(np.exp(d)) + def root_function_autograd(x, d): + return x - anp.log(anp.exp(d)) + value = np.random.normal(0, 100) my_obs = pe.pseudo_Obs(value, 0.1, 't') + # Since autograd 1.9.0 plain numpy ufuncs are dispatched to the autograd + # wrappers, so a root function using numpy now yields the same result as + # one using autograd.numpy. + assert pe.roots.find_root(my_obs, root_function) == pe.roots.find_root(my_obs, root_function_autograd) + + # A function that genuinely cannot be traced by autograd must still raise. + def root_invalid(x, d): + return x - np.float64(d) + with pytest.raises(Exception): - my_root = pe.roots.find_root(my_obs, root_function) + pe.roots.find_root(my_obs, root_invalid) def test_root_multi_parameter():