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feat: optimized calculation of the inverse hessian for error propagation

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in fits.
This commit is contained in:
Fabian Joswig 2022-05-26 10:19:39 +01:00
parent e6aa679170
commit ff5540d667
1 changed files with 15 additions and 23 deletions
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38
pyerrors/fits.py
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@ -267,16 +267,6 @@ def total_least_squares(x, y, func, silent=False, **kwargs):
hess = jacobian(jacobian(odr_chisquare))(np.concatenate((fitp, out.xplus.ravel())))
except TypeError:
raise Exception("It is required to use autograd.numpy instead of numpy within fit functions, see the documentation for details.") from None
condn = np.linalg.cond(hess)
if condn > 1e8:
warnings.warn("Hessian matrix might be ill-conditioned ({0:1.2e}), error propagation might be unreliable.\n \
Maybe try rescaling the problem such that all parameters are of O(1).".format(condn), RuntimeWarning)
try:
hess_inv = np.linalg.inv(hess)
except np.linalg.LinAlgError:
raise Exception("Cannot invert hessian matrix.")
except Exception:
raise Exception("Unkown error in connection with Hessian inverse.")
def odr_chisquare_compact_x(d):
model = func(d[:n_parms], d[n_parms:n_parms + m].reshape(x_shape))
@ -285,7 +275,11 @@ def total_least_squares(x, y, func, silent=False, **kwargs):
jac_jac_x = jacobian(jacobian(odr_chisquare_compact_x))(np.concatenate((fitp, out.xplus.ravel(), x_f.ravel())))
deriv_x = -hess_inv @ jac_jac_x[:n_parms + m, n_parms + m:]
# Compute hess^{-1} @ jac_jac_x[:n_parms + m, n_parms + m:] using LAPACK dgesv
try:
deriv_x = -scipy.linalg.solve(hess, jac_jac_x[:n_parms + m, n_parms + m:])
except np.linalg.LinAlgError:
raise Exception("Cannot invert hessian matrix.")
def odr_chisquare_compact_y(d):
model = func(d[:n_parms], d[n_parms:n_parms + m].reshape(x_shape))
@ -294,7 +288,11 @@ def total_least_squares(x, y, func, silent=False, **kwargs):
jac_jac_y = jacobian(jacobian(odr_chisquare_compact_y))(np.concatenate((fitp, out.xplus.ravel(), y_f)))
deriv_y = -hess_inv @ jac_jac_y[:n_parms + m, n_parms + m:]
# Compute hess^{-1} @ jac_jac_y[:n_parms + m, n_parms + m:] using LAPACK dgesv
try:
deriv_y = -scipy.linalg.solve(hess, jac_jac_y[:n_parms + m, n_parms + m:])
except np.linalg.LinAlgError:
raise Exception("Cannot invert hessian matrix.")
result = []
for i in range(n_parms):
@ -560,16 +558,6 @@ def _standard_fit(x, y, func, silent=False, **kwargs):
hess = jacobian(jacobian(chisqfunc))(fitp)
except TypeError:
raise Exception("It is required to use autograd.numpy instead of numpy within fit functions, see the documentation for details.") from None
condn = np.linalg.cond(hess)
if condn > 1e8:
warnings.warn("Hessian matrix might be ill-conditioned ({0:1.2e}), error propagation might be unreliable.\n \
Maybe try rescaling the problem such that all parameters are of O(1).".format(condn), RuntimeWarning)
try:
hess_inv = np.linalg.inv(hess)
except np.linalg.LinAlgError:
raise Exception("Cannot invert hessian matrix.")
except Exception:
raise Exception("Unkown error in connection with Hessian inverse.")
if kwargs.get('correlated_fit') is True:
def chisqfunc_compact(d):
@ -585,7 +573,11 @@ def _standard_fit(x, y, func, silent=False, **kwargs):
jac_jac = jacobian(jacobian(chisqfunc_compact))(np.concatenate((fitp, y_f)))
deriv = -hess_inv @ jac_jac[:n_parms, n_parms:]
# Compute hess^{-1} @ jac_jac[:n_parms, n_parms:] using LAPACK dgesv
try:
deriv = -scipy.linalg.solve(hess, jac_jac[:n_parms, n_parms:])
except np.linalg.LinAlgError:
raise Exception("Cannot invert hessian matrix.")
result = []
for i in range(n_parms):