def test_parallel_objective(self):
# check that a parallel objective works without issue
# (it could be possible that parallel evaluation fails at a higher
# level in e.g. emcee or in scipy.optimize.differential_evolution)
model = self.model361
model.threads = 2
objective = Objective(
model,
(self.qvals361, self.rvals361, self.evals361),
transform=Transform("logY"),
)
p0 = np.array(objective.varying_parameters())
cov = objective.covar()
walkers = np.random.multivariate_normal(np.atleast_1d(p0),
np.atleast_2d(cov),
size=(100))
map_logl = np.array(list(map(objective.logl, walkers)))
map_chi2 = np.array(list(map(objective.chisqr, walkers)))
wf = Wrapper_fn2(model.model, p0)
map_mod = np.array(list(map(wf, walkers)))
with MapWrapper(2) as g:
mapw_mod = g(wf, walkers)
mapw_logl = g(objective.logl, walkers)
mapw_chi2 = g(objective.chisqr, walkers)
assert_allclose(mapw_logl, map_logl)
assert_allclose(mapw_chi2, map_chi2)
assert_allclose(mapw_mod, map_mod)
def test_multidimensionality(self):
# Check that ND data can be used with an objective/model/data
# (or at least it doesn't stand in the way)
rng = np.random.default_rng()
x = rng.uniform(size=100).reshape(50, 2)
desired = line_ND(x, self.p)
assert desired.shape == (50, 2)
data = Data1D((x, desired))
model = Model(self.p, fitfunc=line_ND)
y = model(x)
assert_allclose(y, desired)
objective = Objective(model, data)
assert_allclose(objective.chisqr(), 0)
assert_allclose(objective.generative(), desired)
assert_allclose(objective.residuals(), 0)
assert objective.residuals().shape == (50, 2)
objective.logl()
objective.logpost()
covar = objective.covar()
assert covar.shape == (2, 2)
def test_covar(self):
# checks objective.covar against optimize.least_squares covariance.
path = os.path.dirname(os.path.abspath(__file__))
theoretical = np.loadtxt(os.path.join(path, 'gauss_data.txt'))
xvals, yvals, evals = np.hsplit(theoretical, 3)
xvals = xvals.flatten()
yvals = yvals.flatten()
evals = evals.flatten()
p0 = np.array([0.1, 20., 0.1, 0.1])
names = ['bkg', 'A', 'x0', 'width']
bounds = [(-1, 1), (0, 30), (-5., 5.), (0.001, 2)]
params = Parameters(name="gauss_params")
for p, name, bound in zip(p0, names, bounds):
param = Parameter(p, name=name)
param.range(*bound)
param.vary = True
params.append(param)
model = Model(params, fitfunc=gauss)
data = Data1D((xvals, yvals, evals))
objective = Objective(model, data)
# first calculate least_squares jac/hess/covariance matrices
res = least_squares(objective.residuals,
np.array(params),
jac='3-point')
hess_least_squares = np.matmul(res.jac.T, res.jac)
covar_least_squares = np.linalg.inv(hess_least_squares)
# now calculate corresponding matrices by hand, to see if the approach
# concurs with least_squares
objective.setp(res.x)
_pvals = np.array(res.x)
def residuals_scaler(vals):
return np.squeeze(objective.residuals(_pvals * vals))
jac = approx_derivative(residuals_scaler, np.ones_like(_pvals))
hess = np.matmul(jac.T, jac)
covar = np.linalg.inv(hess)
covar = covar * np.atleast_2d(_pvals) * np.atleast_2d(_pvals).T
assert_allclose(covar, covar_least_squares)
# check that objective.covar corresponds to the least_squares
# covariance matrix
objective.setp(res.x)
_pvals = np.array(res.x)
covar_objective = objective.covar()
assert_allclose(covar_objective, covar_least_squares)
# now see what happens with a parameter that has no effect on residuals
param = Parameter(1.234, name='dummy')
param.vary = True
params.append(param)
from pytest import raises
with raises(LinAlgError):
objective.covar()
