import pytest import numpy as np from numpy.testing import assert_allclose, assert_equal, assert_array_less from scipy import stats, special import scipy._lib._elementwise_iterative_method as eim from scipy.conftest import array_api_compatible from scipy._lib._array_api import (array_namespace, xp_assert_close, xp_assert_equal, xp_assert_less, xp_minimum, is_numpy, is_cupy) from scipy.optimize._chandrupatla import (_chandrupatla_minimize, _chandrupatla as _chandrupatla_root) from scipy.optimize._tstutils import _CHANDRUPATLA_TESTS from itertools import permutations from .test_zeros import TestScalarRootFinders def f1(x): return 100*(1 - x**3.)**2 + (1-x**2.) + 2*(1-x)**2. def f2(x): return 5 + (x - 2.)**6 def f3(x): return np.exp(x) - 5*x def f4(x): return x**5. - 5*x**3. - 20.*x + 5. def f5(x): return 8*x**3 - 2*x**2 - 7*x + 3 def _bracket_minimum(func, x1, x2): phi = 1.61803398875 maxiter = 100 f1 = func(x1) f2 = func(x2) step = x2 - x1 x1, x2, f1, f2, step = ((x2, x1, f2, f1, -step) if f2 > f1 else (x1, x2, f1, f2, step)) for i in range(maxiter): step *= phi x3 = x2 + step f3 = func(x3) if f3 < f2: x1, x2, f1, f2 = x2, x3, f2, f3 else: break return x1, x2, x3, f1, f2, f3 cases = [ (f1, -1, 11), (f1, -2, 13), (f1, -4, 13), (f1, -8, 15), (f1, -16, 16), (f1, -32, 19), (f1, -64, 20), (f1, -128, 21), (f1, -256, 21), (f1, -512, 19), (f1, -1024, 24), (f2, -1, 8), (f2, -2, 6), (f2, -4, 6), (f2, -8, 7), (f2, -16, 8), (f2, -32, 8), (f2, -64, 9), (f2, -128, 11), (f2, -256, 13), (f2, -512, 12), (f2, -1024, 13), (f3, -1, 11), (f3, -2, 11), (f3, -4, 11), (f3, -8, 10), (f3, -16, 14), (f3, -32, 12), (f3, -64, 15), (f3, -128, 18), (f3, -256, 18), (f3, -512, 19), (f3, -1024, 19), (f4, -0.05, 9), (f4, -0.10, 11), (f4, -0.15, 11), (f4, -0.20, 11), (f4, -0.25, 11), (f4, -0.30, 9), (f4, -0.35, 9), (f4, -0.40, 9), (f4, -0.45, 10), (f4, -0.50, 10), (f4, -0.55, 10), (f5, -0.05, 6), (f5, -0.10, 7), (f5, -0.15, 8), (f5, -0.20, 10), (f5, -0.25, 9), (f5, -0.30, 8), (f5, -0.35, 7), (f5, -0.40, 7), (f5, -0.45, 9), (f5, -0.50, 9), (f5, -0.55, 8) ] class TestChandrupatlaMinimize: def f(self, x, loc): dist = stats.norm() return -dist.pdf(x - loc) @pytest.mark.parametrize('loc', [0.6, np.linspace(-1.05, 1.05, 10)]) def test_basic(self, loc): # Find mode of normal distribution. Compare mode against location # parameter and value of pdf at mode against expected pdf. res = _chandrupatla_minimize(self.f, -5, 0, 5, args=(loc,)) ref = loc np.testing.assert_allclose(res.x, ref, rtol=1e-6) np.testing.assert_allclose(res.fun, -stats.norm.pdf(0), atol=0, rtol=0) assert res.x.shape == np.shape(ref) @pytest.mark.parametrize('shape', [tuple(), (12,), (3, 4), (3, 2, 2)]) def test_vectorization(self, shape): # Test for correct functionality, output shapes, and dtypes for various # input shapes. loc = np.linspace(-0.05, 1.05, 12).reshape(shape) if shape else 0.6 args = (loc,) @np.vectorize def chandrupatla_single(loc_single): return _chandrupatla_minimize(self.f, -5, 0, 5, args=(loc_single,)) def f(*args, **kwargs): f.f_evals += 1 return self.f(*args, **kwargs) f.f_evals = 0 res = _chandrupatla_minimize(f, -5, 0, 5, args=args) refs = chandrupatla_single(loc).ravel() ref_x = [ref.x for ref in refs] assert_allclose(res.x.ravel(), ref_x) assert_equal(res.x.shape, shape) ref_fun = [ref.fun for ref in refs] assert_allclose(res.fun.ravel(), ref_fun) assert_equal(res.fun.shape, shape) assert_equal(res.fun, self.f(res.x, *args)) ref_success = [ref.success for ref in refs] assert_equal(res.success.ravel(), ref_success) assert_equal(res.success.shape, shape) assert np.issubdtype(res.success.dtype, np.bool_) ref_flag = [ref.status for ref in refs] assert_equal(res.status.ravel(), ref_flag) assert_equal(res.status.shape, shape) assert np.issubdtype(res.status.dtype, np.integer) ref_nfev = [ref.nfev for ref in refs] assert_equal(res.nfev.ravel(), ref_nfev) assert_equal(np.max(res.nfev), f.f_evals) assert_equal(res.nfev.shape, res.fun.shape) assert np.issubdtype(res.nfev.dtype, np.integer) ref_nit = [ref.nit for ref in refs] assert_equal(res.nit.ravel(), ref_nit) assert_equal(np.max(res.nit), f.f_evals-3) assert_equal(res.nit.shape, res.fun.shape) assert np.issubdtype(res.nit.dtype, np.integer) ref_xl = [ref.xl for ref in refs] assert_allclose(res.xl.ravel(), ref_xl) assert_equal(res.xl.shape, shape) ref_xm = [ref.xm for ref in refs] assert_allclose(res.xm.ravel(), ref_xm) assert_equal(res.xm.shape, shape) ref_xr = [ref.xr for ref in refs] assert_allclose(res.xr.ravel(), ref_xr) assert_equal(res.xr.shape, shape) ref_fl = [ref.fl for ref in refs] assert_allclose(res.fl.ravel(), ref_fl) assert_equal(res.fl.shape, shape) assert_allclose(res.fl, self.f(res.xl, *args)) ref_fm = [ref.fm for ref in refs] assert_allclose(res.fm.ravel(), ref_fm) assert_equal(res.fm.shape, shape) assert_allclose(res.fm, self.f(res.xm, *args)) ref_fr = [ref.fr for ref in refs] assert_allclose(res.fr.ravel(), ref_fr) assert_equal(res.fr.shape, shape) assert_allclose(res.fr, self.f(res.xr, *args)) def test_flags(self): # Test cases that should produce different status flags; show that all # can be produced simultaneously. def f(xs, js): funcs = [lambda x: (x - 2.5) ** 2, lambda x: x - 10, lambda x: (x - 2.5) ** 4, lambda x: np.nan] return [funcs[j](x) for x, j in zip(xs, js)] args = (np.arange(4, dtype=np.int64),) res = _chandrupatla_minimize(f, [0]*4, [2]*4, [np.pi]*4, args=args, maxiter=10) ref_flags = np.array([eim._ECONVERGED, eim._ESIGNERR, eim._ECONVERR, eim._EVALUEERR]) assert_equal(res.status, ref_flags) def test_convergence(self): # Test that the convergence tolerances behave as expected rng = np.random.default_rng(2585255913088665241) p = rng.random(size=3) bracket = (-5, 0, 5) args = (p,) kwargs0 = dict(args=args, xatol=0, xrtol=0, fatol=0, frtol=0) kwargs = kwargs0.copy() kwargs['xatol'] = 1e-3 res1 = _chandrupatla_minimize(self.f, *bracket, **kwargs) j1 = abs(res1.xr - res1.xl) assert_array_less(j1, 4*kwargs['xatol']) kwargs['xatol'] = 1e-6 res2 = _chandrupatla_minimize(self.f, *bracket, **kwargs) j2 = abs(res2.xr - res2.xl) assert_array_less(j2, 4*kwargs['xatol']) assert_array_less(j2, j1) kwargs = kwargs0.copy() kwargs['xrtol'] = 1e-3 res1 = _chandrupatla_minimize(self.f, *bracket, **kwargs) j1 = abs(res1.xr - res1.xl) assert_array_less(j1, 4*kwargs['xrtol']*abs(res1.x)) kwargs['xrtol'] = 1e-6 res2 = _chandrupatla_minimize(self.f, *bracket, **kwargs) j2 = abs(res2.xr - res2.xl) assert_array_less(j2, 4*kwargs['xrtol']*abs(res2.x)) assert_array_less(j2, j1) kwargs = kwargs0.copy() kwargs['fatol'] = 1e-3 res1 = _chandrupatla_minimize(self.f, *bracket, **kwargs) h1 = abs(res1.fl - 2 * res1.fm + res1.fr) assert_array_less(h1, 2*kwargs['fatol']) kwargs['fatol'] = 1e-6 res2 = _chandrupatla_minimize(self.f, *bracket, **kwargs) h2 = abs(res2.fl - 2 * res2.fm + res2.fr) assert_array_less(h2, 2*kwargs['fatol']) assert_array_less(h2, h1) kwargs = kwargs0.copy() kwargs['frtol'] = 1e-3 res1 = _chandrupatla_minimize(self.f, *bracket, **kwargs) h1 = abs(res1.fl - 2 * res1.fm + res1.fr) assert_array_less(h1, 2*kwargs['frtol']*abs(res1.fun)) kwargs['frtol'] = 1e-6 res2 = _chandrupatla_minimize(self.f, *bracket, **kwargs) h2 = abs(res2.fl - 2 * res2.fm + res2.fr) assert_array_less(h2, 2*kwargs['frtol']*abs(res2.fun)) assert_array_less(h2, h1) def test_maxiter_callback(self): # Test behavior of `maxiter` parameter and `callback` interface loc = 0.612814 bracket = (-5, 0, 5) maxiter = 5 res = _chandrupatla_minimize(self.f, *bracket, args=(loc,), maxiter=maxiter) assert not np.any(res.success) assert np.all(res.nfev == maxiter+3) assert np.all(res.nit == maxiter) def callback(res): callback.iter += 1 callback.res = res assert hasattr(res, 'x') if callback.iter == 0: # callback is called once with initial bracket assert (res.xl, res.xm, res.xr) == bracket else: changed_xr = (res.xl == callback.xl) & (res.xr != callback.xr) changed_xl = (res.xl != callback.xl) & (res.xr == callback.xr) assert np.all(changed_xr | changed_xl) callback.xl = res.xl callback.xr = res.xr assert res.status == eim._EINPROGRESS assert_equal(self.f(res.xl, loc), res.fl) assert_equal(self.f(res.xm, loc), res.fm) assert_equal(self.f(res.xr, loc), res.fr) assert_equal(self.f(res.x, loc), res.fun) if callback.iter == maxiter: raise StopIteration callback.xl = np.nan callback.xr = np.nan callback.iter = -1 # callback called once before first iteration callback.res = None res2 = _chandrupatla_minimize(self.f, *bracket, args=(loc,), callback=callback) # terminating with callback is identical to terminating due to maxiter # (except for `status`) for key in res.keys(): if key == 'status': assert res[key] == eim._ECONVERR assert callback.res[key] == eim._EINPROGRESS assert res2[key] == eim._ECALLBACK else: assert res2[key] == callback.res[key] == res[key] @pytest.mark.parametrize('case', cases) def test_nit_expected(self, case): # Test that `_chandrupatla` implements Chandrupatla's algorithm: # in all 55 test cases, the number of iterations performed # matches the number reported in the original paper. func, x1, nit = case # Find bracket using the algorithm in the paper step = 0.2 x2 = x1 + step x1, x2, x3, f1, f2, f3 = _bracket_minimum(func, x1, x2) # Use tolerances from original paper xatol = 0.0001 fatol = 0.000001 xrtol = 1e-16 frtol = 1e-16 res = _chandrupatla_minimize(func, x1, x2, x3, xatol=xatol, fatol=fatol, xrtol=xrtol, frtol=frtol) assert_equal(res.nit, nit) @pytest.mark.parametrize("loc", (0.65, [0.65, 0.7])) @pytest.mark.parametrize("dtype", (np.float16, np.float32, np.float64)) def test_dtype(self, loc, dtype): # Test that dtypes are preserved loc = dtype(loc) def f(x, loc): assert x.dtype == dtype return ((x - loc) ** 2).astype(dtype) res = _chandrupatla_minimize(f, dtype(-3), dtype(1), dtype(5), args=(loc,)) assert res.x.dtype == dtype assert_allclose(res.x, loc, rtol=np.sqrt(np.finfo(dtype).eps)) def test_input_validation(self): # Test input validation for appropriate error messages message = '`func` must be callable.' with pytest.raises(ValueError, match=message): _chandrupatla_minimize(None, -4, 0, 4) message = 'Abscissae and function output must be real numbers.' with pytest.raises(ValueError, match=message): _chandrupatla_minimize(lambda x: x, -4+1j, 0, 4) message = "shape mismatch: objects cannot be broadcast" # raised by `np.broadcast, but the traceback is readable IMO with pytest.raises(ValueError, match=message): _chandrupatla_minimize(lambda x: x, [-2, -3], [0, 0], [3, 4, 5]) message = "The shape of the array returned by `func` must be the same" with pytest.raises(ValueError, match=message): _chandrupatla_minimize(lambda x: [x[0], x[1], x[1]], [-3, -3], [0, 0], [5, 5]) message = 'Tolerances must be non-negative scalars.' with pytest.raises(ValueError, match=message): _chandrupatla_minimize(lambda x: x, -4, 0, 4, xatol=-1) with pytest.raises(ValueError, match=message): _chandrupatla_minimize(lambda x: x, -4, 0, 4, xrtol=np.nan) with pytest.raises(ValueError, match=message): _chandrupatla_minimize(lambda x: x, -4, 0, 4, fatol='ekki') with pytest.raises(ValueError, match=message): _chandrupatla_minimize(lambda x: x, -4, 0, 4, frtol=np.nan) message = '`maxiter` must be a non-negative integer.' with pytest.raises(ValueError, match=message): _chandrupatla_minimize(lambda x: x, -4, 0, 4, maxiter=1.5) with pytest.raises(ValueError, match=message): _chandrupatla_minimize(lambda x: x, -4, 0, 4, maxiter=-1) message = '`callback` must be callable.' with pytest.raises(ValueError, match=message): _chandrupatla_minimize(lambda x: x, -4, 0, 4, callback='shrubbery') def test_bracket_order(self): # Confirm that order of points in bracket doesn't matter loc = np.linspace(-1, 1, 6)[:, np.newaxis] brackets = np.array(list(permutations([-5, 0, 5]))).T res = _chandrupatla_minimize(self.f, *brackets, args=(loc,)) assert np.all(np.isclose(res.x, loc) | (res.fun == self.f(loc, loc))) ref = res.x[:, 0] # all columns should be the same assert_allclose(*np.broadcast_arrays(res.x.T, ref), rtol=1e-15) def test_special_cases(self): # Test edge cases and other special cases # Test that integers are not passed to `f` # (otherwise this would overflow) def f(x): assert np.issubdtype(x.dtype, np.floating) return (x-1) ** 100 with np.errstate(invalid='ignore'): res = _chandrupatla_minimize(f, -7, 0, 8, fatol=0, frtol=0) assert res.success assert_allclose(res.x, 1, rtol=1e-3) assert_equal(res.fun, 0) # Test that if all elements of bracket equal minimizer, algorithm # reports convergence def f(x): return (x-1)**2 res = _chandrupatla_minimize(f, 1, 1, 1) assert res.success assert_equal(res.x, 1) # Test maxiter = 0. Should do nothing to bracket. def f(x): return (x-1)**2 bracket = (-3, 1.1, 5) res = _chandrupatla_minimize(f, *bracket, maxiter=0) assert res.xl, res.xr == bracket assert res.nit == 0 assert res.nfev == 3 assert res.status == -2 assert res.x == 1.1 # best so far # Test scalar `args` (not in tuple) def f(x, c): return (x-c)**2 - 1 res = _chandrupatla_minimize(f, -1, 0, 1, args=1/3) assert_allclose(res.x, 1/3) # Test zero tolerances # TODO: fatol/frtol = 0? def f(x): return -np.sin(x) res = _chandrupatla_minimize(f, 0, 1, np.pi, xatol=0, xrtol=0, fatol=0, frtol=0) assert res.success # found a minimum exactly (according to floating point arithmetic) assert res.xl < res.xm < res.xr assert f(res.xl) == f(res.xm) == f(res.xr) @array_api_compatible @pytest.mark.usefixtures("skip_xp_backends") @pytest.mark.skip_xp_backends('array_api_strict', 'jax.numpy', reasons=['Currently uses fancy indexing assignment.', 'JAX arrays do not support item assignment.']) class TestChandrupatla(TestScalarRootFinders): def f(self, q, p): return special.ndtr(q) - p @pytest.mark.parametrize('p', [0.6, np.linspace(-0.05, 1.05, 10)]) def test_basic(self, p, xp): # Invert distribution CDF and compare against distrtibution `ppf` a, b = xp.asarray(-5.), xp.asarray(5.) res = _chandrupatla_root(self.f, a, b, args=(xp.asarray(p),)) ref = xp.asarray(stats.norm().ppf(p), dtype=xp.asarray(p).dtype) xp_assert_close(res.x, ref) @pytest.mark.parametrize('shape', [tuple(), (12,), (3, 4), (3, 2, 2)]) def test_vectorization(self, shape, xp): # Test for correct functionality, output shapes, and dtypes for various # input shapes. p = (np.linspace(-0.05, 1.05, 12).reshape(shape) if shape else np.float64(0.6)) p_xp = xp.asarray(p) args_xp = (p_xp,) dtype = p_xp.dtype xp_test = array_namespace(p_xp) # need xp.bool @np.vectorize def chandrupatla_single(p): return _chandrupatla_root(self.f, -5, 5, args=(p,)) def f(*args, **kwargs): f.f_evals += 1 return self.f(*args, **kwargs) f.f_evals = 0 res = _chandrupatla_root(f, xp.asarray(-5.), xp.asarray(5.), args=args_xp) refs = chandrupatla_single(p).ravel() ref_x = [ref.x for ref in refs] ref_x = xp.reshape(xp.asarray(ref_x, dtype=dtype), shape) xp_assert_close(res.x, ref_x) ref_fun = [ref.fun for ref in refs] ref_fun = xp.reshape(xp.asarray(ref_fun, dtype=dtype), shape) xp_assert_close(res.fun, ref_fun, atol=1e-15) xp_assert_equal(res.fun, self.f(res.x, *args_xp)) ref_success = [bool(ref.success) for ref in refs] ref_success = xp.reshape(xp.asarray(ref_success, dtype=xp_test.bool), shape) xp_assert_equal(res.success, ref_success) ref_flag = [ref.status for ref in refs] ref_flag = xp.reshape(xp.asarray(ref_flag, dtype=xp.int32), shape) xp_assert_equal(res.status, ref_flag) ref_nfev = [ref.nfev for ref in refs] ref_nfev = xp.reshape(xp.asarray(ref_nfev, dtype=xp.int32), shape) if is_numpy(xp): xp_assert_equal(res.nfev, ref_nfev) assert xp.max(res.nfev) == f.f_evals else: # different backend may lead to different nfev assert res.nfev.shape == shape assert res.nfev.dtype == xp.int32 ref_nit = [ref.nit for ref in refs] ref_nit = xp.reshape(xp.asarray(ref_nit, dtype=xp.int32), shape) if is_numpy(xp): xp_assert_equal(res.nit, ref_nit) assert xp.max(res.nit) == f.f_evals-2 else: assert res.nit.shape == shape assert res.nit.dtype == xp.int32 ref_xl = [ref.xl for ref in refs] ref_xl = xp.reshape(xp.asarray(ref_xl, dtype=dtype), shape) xp_assert_close(res.xl, ref_xl) ref_xr = [ref.xr for ref in refs] ref_xr = xp.reshape(xp.asarray(ref_xr, dtype=dtype), shape) xp_assert_close(res.xr, ref_xr) xp_assert_less(res.xl, res.xr) finite = xp.isfinite(res.x) assert xp.all((res.x[finite] == res.xl[finite]) | (res.x[finite] == res.xr[finite])) # PyTorch and CuPy don't solve to the same accuracy as NumPy - that's OK. atol = 1e-15 if is_numpy(xp) else 1e-9 ref_fl = [ref.fl for ref in refs] ref_fl = xp.reshape(xp.asarray(ref_fl, dtype=dtype), shape) xp_assert_close(res.fl, ref_fl, atol=atol) xp_assert_equal(res.fl, self.f(res.xl, *args_xp)) ref_fr = [ref.fr for ref in refs] ref_fr = xp.reshape(xp.asarray(ref_fr, dtype=dtype), shape) xp_assert_close(res.fr, ref_fr, atol=atol) xp_assert_equal(res.fr, self.f(res.xr, *args_xp)) assert xp.all(xp.abs(res.fun[finite]) == xp_minimum(xp.abs(res.fl[finite]), xp.abs(res.fr[finite]))) def test_flags(self, xp): # Test cases that should produce different status flags; show that all # can be produced simultaneously. def f(xs, js): # Note that full_like and int(j) shouldn't really be required. CuPy # is just really picky here, so I'm making it a special case to # make sure the other backends work when the user is less careful. assert js.dtype == xp.int64 if is_cupy(xp): funcs = [lambda x: x - 2.5, lambda x: x - 10, lambda x: (x - 0.1)**3, lambda x: xp.full_like(x, xp.nan)] return [funcs[int(j)](x) for x, j in zip(xs, js)] funcs = [lambda x: x - 2.5, lambda x: x - 10, lambda x: (x - 0.1) ** 3, lambda x: xp.nan] return [funcs[j](x) for x, j in zip(xs, js)] args = (xp.arange(4, dtype=xp.int64),) a, b = xp.asarray([0.]*4), xp.asarray([xp.pi]*4) res = _chandrupatla_root(f, a, b, args=args, maxiter=2) ref_flags = xp.asarray([eim._ECONVERGED, eim._ESIGNERR, eim._ECONVERR, eim._EVALUEERR], dtype=xp.int32) xp_assert_equal(res.status, ref_flags) def test_convergence(self, xp): # Test that the convergence tolerances behave as expected rng = np.random.default_rng(2585255913088665241) p = xp.asarray(rng.random(size=3)) bracket = (-xp.asarray(5.), xp.asarray(5.)) args = (p,) kwargs0 = dict(args=args, xatol=0, xrtol=0, fatol=0, frtol=0) kwargs = kwargs0.copy() kwargs['xatol'] = 1e-3 res1 = _chandrupatla_root(self.f, *bracket, **kwargs) xp_assert_less(res1.xr - res1.xl, xp.full_like(p, 1e-3)) kwargs['xatol'] = 1e-6 res2 = _chandrupatla_root(self.f, *bracket, **kwargs) xp_assert_less(res2.xr - res2.xl, xp.full_like(p, 1e-6)) xp_assert_less(res2.xr - res2.xl, res1.xr - res1.xl) kwargs = kwargs0.copy() kwargs['xrtol'] = 1e-3 res1 = _chandrupatla_root(self.f, *bracket, **kwargs) xp_assert_less(res1.xr - res1.xl, 1e-3 * xp.abs(res1.x)) kwargs['xrtol'] = 1e-6 res2 = _chandrupatla_root(self.f, *bracket, **kwargs) xp_assert_less(res2.xr - res2.xl, 1e-6 * xp.abs(res2.x)) xp_assert_less(res2.xr - res2.xl, res1.xr - res1.xl) kwargs = kwargs0.copy() kwargs['fatol'] = 1e-3 res1 = _chandrupatla_root(self.f, *bracket, **kwargs) xp_assert_less(xp.abs(res1.fun), xp.full_like(p, 1e-3)) kwargs['fatol'] = 1e-6 res2 = _chandrupatla_root(self.f, *bracket, **kwargs) xp_assert_less(xp.abs(res2.fun), xp.full_like(p, 1e-6)) xp_assert_less(xp.abs(res2.fun), xp.abs(res1.fun)) kwargs = kwargs0.copy() kwargs['frtol'] = 1e-3 x1, x2 = bracket f0 = xp_minimum(xp.abs(self.f(x1, *args)), xp.abs(self.f(x2, *args))) res1 = _chandrupatla_root(self.f, *bracket, **kwargs) xp_assert_less(xp.abs(res1.fun), 1e-3*f0) kwargs['frtol'] = 1e-6 res2 = _chandrupatla_root(self.f, *bracket, **kwargs) xp_assert_less(xp.abs(res2.fun), 1e-6*f0) xp_assert_less(xp.abs(res2.fun), xp.abs(res1.fun)) def test_maxiter_callback(self, xp): # Test behavior of `maxiter` parameter and `callback` interface p = xp.asarray(0.612814) bracket = (xp.asarray(-5.), xp.asarray(5.)) maxiter = 5 def f(q, p): res = special.ndtr(q) - p f.x = q f.fun = res return res f.x = None f.fun = None res = _chandrupatla_root(f, *bracket, args=(p,), maxiter=maxiter) assert not xp.any(res.success) assert xp.all(res.nfev == maxiter+2) assert xp.all(res.nit == maxiter) def callback(res): callback.iter += 1 callback.res = res assert hasattr(res, 'x') if callback.iter == 0: # callback is called once with initial bracket assert (res.xl, res.xr) == bracket else: changed = (((res.xl == callback.xl) & (res.xr != callback.xr)) | ((res.xl != callback.xl) & (res.xr == callback.xr))) assert xp.all(changed) callback.xl = res.xl callback.xr = res.xr assert res.status == eim._EINPROGRESS xp_assert_equal(self.f(res.xl, p), res.fl) xp_assert_equal(self.f(res.xr, p), res.fr) xp_assert_equal(self.f(res.x, p), res.fun) if callback.iter == maxiter: raise StopIteration callback.iter = -1 # callback called once before first iteration callback.res = None callback.xl = None callback.xr = None res2 = _chandrupatla_root(f, *bracket, args=(p,), callback=callback) # terminating with callback is identical to terminating due to maxiter # (except for `status`) for key in res.keys(): if key == 'status': xp_assert_equal(res[key], xp.asarray(eim._ECONVERR, dtype=xp.int32)) xp_assert_equal(res2[key], xp.asarray(eim._ECALLBACK, dtype=xp.int32)) elif key.startswith('_'): continue else: xp_assert_equal(res2[key], res[key]) @pytest.mark.parametrize('case', _CHANDRUPATLA_TESTS) def test_nit_expected(self, case, xp): # Test that `_chandrupatla` implements Chandrupatla's algorithm: # in all 40 test cases, the number of iterations performed # matches the number reported in the original paper. f, bracket, root, nfeval, id = case # Chandrupatla's criterion is equivalent to # abs(x2-x1) < 4*abs(xmin)*xrtol + xatol, but we use the more standard # abs(x2-x1) < abs(xmin)*xrtol + xatol. Therefore, set xrtol to 4x # that used by Chandrupatla in tests. bracket = (xp.asarray(bracket[0], dtype=xp.float64), xp.asarray(bracket[1], dtype=xp.float64)) root = xp.asarray(root, dtype=xp.float64) res = _chandrupatla_root(f, *bracket, xrtol=4e-10, xatol=1e-5) xp_assert_close(res.fun, xp.asarray(f(root), dtype=xp.float64), rtol=1e-8, atol=2e-3) xp_assert_equal(res.nfev, xp.asarray(nfeval, dtype=xp.int32)) @pytest.mark.parametrize("root", (0.622, [0.622, 0.623])) @pytest.mark.parametrize("dtype", ('float16', 'float32', 'float64')) def test_dtype(self, root, dtype, xp): # Test that dtypes are preserved not_numpy = not is_numpy(xp) if not_numpy and dtype == 'float16': pytest.skip("`float16` dtype only supported for NumPy arrays.") dtype = getattr(xp, dtype, None) if dtype is None: pytest.skip(f"{xp} does not support {dtype}") def f(x, root): res = (x - root) ** 3. if is_numpy(xp): # NumPy does not preserve dtype return xp.asarray(res, dtype=dtype) return res a, b = xp.asarray(-3, dtype=dtype), xp.asarray(3, dtype=dtype) root = xp.asarray(root, dtype=dtype) res = _chandrupatla_root(f, a, b, args=(root,), xatol=1e-3) try: xp_assert_close(res.x, root, atol=1e-3) except AssertionError: assert res.x.dtype == dtype xp.all(res.fun == 0) def test_input_validation(self, xp): # Test input validation for appropriate error messages def func(x): return x message = '`func` must be callable.' with pytest.raises(ValueError, match=message): bracket = xp.asarray(-4), xp.asarray(4) _chandrupatla_root(None, *bracket) message = 'Abscissae and function output must be real numbers.' with pytest.raises(ValueError, match=message): bracket = xp.asarray(-4+1j), xp.asarray(4) _chandrupatla_root(func, *bracket) # raised by `np.broadcast, but the traceback is readable IMO message = "...not be broadcast..." # all messages include this part with pytest.raises((ValueError, RuntimeError), match=message): bracket = xp.asarray([-2, -3]), xp.asarray([3, 4, 5]) _chandrupatla_root(func, *bracket) message = "The shape of the array returned by `func`..." with pytest.raises(ValueError, match=message): bracket = xp.asarray([-3, -3]), xp.asarray([5, 5]) _chandrupatla_root(lambda x: [x[0], x[1], x[1]], *bracket) message = 'Tolerances must be non-negative scalars.' bracket = xp.asarray(-4), xp.asarray(4) with pytest.raises(ValueError, match=message): _chandrupatla_root(func, *bracket, xatol=-1) with pytest.raises(ValueError, match=message): _chandrupatla_root(func, *bracket, xrtol=xp.nan) with pytest.raises(ValueError, match=message): _chandrupatla_root(func, *bracket, fatol='ekki') with pytest.raises(ValueError, match=message): _chandrupatla_root(func, *bracket, frtol=xp.nan) message = '`maxiter` must be a non-negative integer.' with pytest.raises(ValueError, match=message): _chandrupatla_root(func, *bracket, maxiter=1.5) with pytest.raises(ValueError, match=message): _chandrupatla_root(func, *bracket, maxiter=-1) message = '`callback` must be callable.' with pytest.raises(ValueError, match=message): _chandrupatla_root(func, *bracket, callback='shrubbery') def test_special_cases(self, xp): # Test edge cases and other special cases # Test infinite function values def f(x): return 1 / x + 1 - 1 / (-x + 1) a, b = xp.asarray([0.1, 0., 0., 0.1]), xp.asarray([0.9, 1.0, 0.9, 1.0]) with np.errstate(divide='ignore', invalid='ignore'): res = _chandrupatla_root(f, a, b) assert xp.all(res.success) xp_assert_close(res.x[1:], xp.full((3,), res.x[0])) # Test that integers are not passed to `f` # (otherwise this would overflow) xp_test = array_namespace(a) # need isdtype def f(x): assert xp_test.isdtype(x.dtype, "real floating") # this would overflow if x were an xp integer dtype return x ** 31 - 1 # note that all inputs are integer type; result is automatically default float res = _chandrupatla_root(f, xp.asarray(-7), xp.asarray(5)) assert res.success xp_assert_close(res.x, xp.asarray(1.)) # Test that if both ends of bracket equal root, algorithm reports # convergence. def f(x, root): return x**2 - root root = xp.asarray([0, 1]) res = _chandrupatla_root(f, xp.asarray(1), xp.asarray(1), args=(root,)) xp_assert_equal(res.success, xp.asarray([False, True])) xp_assert_equal(res.x, xp.asarray([np.nan, 1.])) def f(x): return 1/x with np.errstate(invalid='ignore'): inf = xp.asarray(xp.inf) res = _chandrupatla_root(f, inf, inf) assert res.success xp_assert_equal(res.x, xp.asarray(np.inf)) # Test maxiter = 0. Should do nothing to bracket. def f(x): return x**3 - 1 a, b = xp.asarray(-3.), xp.asarray(5.) res = _chandrupatla_root(f, a, b, maxiter=0) xp_assert_equal(res.success, xp.asarray(False)) xp_assert_equal(res.status, xp.asarray(-2, dtype=xp.int32)) xp_assert_equal(res.nit, xp.asarray(0, dtype=xp.int32)) xp_assert_equal(res.nfev, xp.asarray(2, dtype=xp.int32)) xp_assert_equal(res.xl, a) xp_assert_equal(res.xr, b) # The `x` attribute is the one with the smaller function value xp_assert_equal(res.x, a) # Reverse bracket; check that this is still true res = _chandrupatla_root(f, -b, -a, maxiter=0) xp_assert_equal(res.x, -a) # Test maxiter = 1 res = _chandrupatla_root(f, a, b, maxiter=1) xp_assert_equal(res.success, xp.asarray(True)) xp_assert_equal(res.status, xp.asarray(0, dtype=xp.int32)) xp_assert_equal(res.nit, xp.asarray(1, dtype=xp.int32)) xp_assert_equal(res.nfev, xp.asarray(3, dtype=xp.int32)) xp_assert_close(res.x, xp.asarray(1.)) # Test scalar `args` (not in tuple) def f(x, c): return c*x - 1 res = _chandrupatla_root(f, xp.asarray(-1), xp.asarray(1), args=xp.asarray(3)) xp_assert_close(res.x, xp.asarray(1/3)) # # TODO: Test zero tolerance # # ~~What's going on here - why are iterations repeated?~~ # # tl goes to zero when xatol=xrtol=0. When function is nearly linear, # # this causes convergence issues. # def f(x): # return np.cos(x) # # res = _chandrupatla_root(f, 0, np.pi, xatol=0, xrtol=0) # assert res.nit < 100 # xp = np.nextafter(res.x, np.inf) # xm = np.nextafter(res.x, -np.inf) # assert np.abs(res.fun) < np.abs(f(xp)) # assert np.abs(res.fun) < np.abs(f(xm))