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refactor: logic in least square fits simplified.
Co-authored-by: Simon Kuberski <simon.kuberski@uni-muenster.de>
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a140b2ab39
commit
2ac38515b6
1 changed files with 30 additions and 37 deletions
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@ -580,6 +580,13 @@ def _combined_fit(x, y, func, silent=False, **kwargs):
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else:
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x0 = [0.1] * n_parms
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def general_chisqfunc_uncorr(p, ivars):
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model = anp.concatenate([anp.array(funcd[key](p, anp.asarray(xd[key]))).reshape(-1) for key in key_ls])
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return ((ivars - model) / dy_f)
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def chisqfunc_uncorr(p):
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return anp.sum(general_chisqfunc_uncorr(p, y_f) ** 2)
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if kwargs.get('correlated_fit') is True:
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corr = covariance(y_all, correlation=True, **kwargs)
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covdiag = np.diag(1 / np.asarray(dy_f))
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@ -591,26 +598,15 @@ def _combined_fit(x, y, func, silent=False, **kwargs):
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chol = np.linalg.cholesky(corr)
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chol_inv = scipy.linalg.solve_triangular(chol, covdiag, lower=True)
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def general_chisqfunc_corr(p, ivars):
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def general_chisqfunc(p, ivars):
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model = anp.concatenate([anp.array(funcd[key](p, anp.asarray(xd[key]))).reshape(-1) for key in key_ls])
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return anp.dot(chol_inv, (ivars - model))
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def general_chisqfunc(p, ivars):
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model = anp.concatenate([anp.array(funcd[key](p, anp.asarray(xd[key]))).reshape(-1) for key in key_ls])
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return ((ivars - model) / dy_f)
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if kwargs.get('correlated_fit') is True:
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def chisqfunc_residuals_corr(p):
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return general_chisqfunc_corr(p, y_f)
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def chisqfunc_corr(p):
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return anp.sum(chisqfunc_residuals_corr(p) ** 2)
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def chisqfunc_residuals(p):
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return general_chisqfunc(p, y_f)
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def chisqfunc(p):
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return anp.sum(chisqfunc_residuals(p) ** 2)
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def chisqfunc(p):
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return anp.sum(general_chisqfunc(p, y_f) ** 2)
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else:
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general_chisqfunc = general_chisqfunc_uncorr
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chisqfunc = chisqfunc_uncorr
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output.method = kwargs.get('method', 'Levenberg-Marquardt')
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if not silent:
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@ -621,17 +617,17 @@ def _combined_fit(x, y, func, silent=False, **kwargs):
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tolerance = 1e-4 # default value of 1e-1 set by iminuit can be problematic
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if 'tol' in kwargs:
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tolerance = kwargs.get('tol')
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fit_result = iminuit.minimize(chisqfunc, x0, tol=tolerance) # Stopping criterion 0.002 * tol * errordef
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fit_result = iminuit.minimize(chisqfunc_uncorr, x0, tol=tolerance) # Stopping criterion 0.002 * tol * errordef
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if kwargs.get('correlated_fit') is True:
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fit_result = iminuit.minimize(chisqfunc_corr, fit_result.x, tol=tolerance)
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fit_result = iminuit.minimize(chisqfunc, fit_result.x, tol=tolerance)
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output.iterations = fit_result.nfev
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else:
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tolerance = 1e-12
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if 'tol' in kwargs:
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tolerance = kwargs.get('tol')
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fit_result = scipy.optimize.minimize(chisqfunc, x0, method=kwargs.get('method'), tol=tolerance)
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fit_result = scipy.optimize.minimize(chisqfunc_uncorr, x0, method=kwargs.get('method'), tol=tolerance)
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if kwargs.get('correlated_fit') is True:
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fit_result = scipy.optimize.minimize(chisqfunc_corr, fit_result.x, method=kwargs.get('method'), tol=tolerance)
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fit_result = scipy.optimize.minimize(chisqfunc, fit_result.x, method=kwargs.get('method'), tol=tolerance)
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output.iterations = fit_result.nit
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chisquare = fit_result.fun
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@ -640,15 +636,19 @@ def _combined_fit(x, y, func, silent=False, **kwargs):
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if 'tol' in kwargs:
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print('tol cannot be set for Levenberg-Marquardt')
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fit_result = scipy.optimize.least_squares(chisqfunc_residuals, x0, method='lm', ftol=1e-15, gtol=1e-15, xtol=1e-15)
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def chisqfunc_residuals_uncorr(p):
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return general_chisqfunc_uncorr(p, y_f)
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fit_result = scipy.optimize.least_squares(chisqfunc_residuals_uncorr, x0, method='lm', ftol=1e-15, gtol=1e-15, xtol=1e-15)
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if kwargs.get('correlated_fit') is True:
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fit_result = scipy.optimize.least_squares(chisqfunc_residuals_corr, fit_result.x, method='lm', ftol=1e-15, gtol=1e-15, xtol=1e-15)
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def chisqfunc_residuals(p):
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return general_chisqfunc(p, y_f)
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fit_result = scipy.optimize.least_squares(chisqfunc_residuals, fit_result.x, method='lm', ftol=1e-15, gtol=1e-15, xtol=1e-15)
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chisquare = np.sum(fit_result.fun ** 2)
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if kwargs.get('correlated_fit') is True:
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assert np.isclose(chisquare, chisqfunc_corr(fit_result.x), atol=1e-14)
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else:
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assert np.isclose(chisquare, chisqfunc(fit_result.x), atol=1e-14)
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assert np.isclose(chisquare, chisqfunc(fit_result.x), atol=1e-14)
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output.iterations = fit_result.nfev
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@ -695,19 +695,12 @@ def _combined_fit(x, y, func, silent=False, **kwargs):
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raise Exception('No y errors available, run the gamma method first.')
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try:
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if kwargs.get('correlated_fit') is True:
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hess = hessian(chisqfunc_corr)(fitp)
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else:
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hess = hessian(chisqfunc)(fitp)
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hess = hessian(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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if kwargs.get('correlated_fit') is True:
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def chisqfunc_compact(d):
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return anp.sum(general_chisqfunc_corr(d[:n_parms], d[n_parms:]) ** 2)
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else:
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def chisqfunc_compact(d):
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return anp.sum(general_chisqfunc(d[:n_parms], d[n_parms:]) ** 2)
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def chisqfunc_compact(d):
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return anp.sum(general_chisqfunc(d[:n_parms], d[n_parms:]) ** 2)
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jac_jac_y = hessian(chisqfunc_compact)(np.concatenate((fitp, y_f)))
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