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