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refactor!: const_par keyword for constrained fits removed from functions

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in fit module.
This commit is contained in:
Fabian Joswig 2022-01-24 17:43:23 +00:00
parent 1c088bb1b4
commit 2a925ba0b8
1 changed files with 17 additions and 65 deletions
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82
pyerrors/fits.py
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@ -113,8 +113,6 @@ def least_squares(x, y, func, priors=None, silent=False, **kwargs):
correlated_fit : bool correlated_fit : bool
If true, use the full correlation matrix in the definition of the chisquare If true, use the full correlation matrix in the definition of the chisquare
(only works for prior==None and when no method is given, at the moment). (only works for prior==None and when no method is given, at the moment).
const_par : list, optional
List of N Obs that are used to constrain the last N fit parameters of func.
''' '''
if priors is not None: if priors is not None:
return _prior_fit(x, y, func, priors, silent=silent, **kwargs) return _prior_fit(x, y, func, priors, silent=silent, **kwargs)
@ -160,8 +158,6 @@ def total_least_squares(x, y, func, silent=False, **kwargs):
corrected by effects caused by correlated input data. corrected by effects caused by correlated input data.
This can take a while as the full correlation matrix This can take a while as the full correlation matrix
has to be calculated (default False). has to be calculated (default False).
const_par : list, optional
List of N Obs that are used to constrain the last N fit parameters of func.
Based on the orthogonal distance regression module of scipy Based on the orthogonal distance regression module of scipy
''' '''
@ -177,17 +173,6 @@ def total_least_squares(x, y, func, silent=False, **kwargs):
if not callable(func): if not callable(func):
raise TypeError('func has to be a function.') raise TypeError('func has to be a function.')
func_aug = func
if 'const_par' in kwargs:
const_par = kwargs['const_par']
if isinstance(const_par, Obs):
const_par = [const_par]
def func(p, x):
return func_aug(np.concatenate((p, [o.value for o in const_par])), x)
else:
const_par = []
for i in range(25): for i in range(25):
try: try:
func(np.arange(i), x.T[0]) func(np.arange(i), x.T[0])
@ -199,8 +184,6 @@ def total_least_squares(x, y, func, silent=False, **kwargs):
n_parms = i n_parms = i
if not silent: if not silent:
print('Fit with', n_parms, 'parameter' + 's' * (n_parms > 1)) print('Fit with', n_parms, 'parameter' + 's' * (n_parms > 1))
if(len(const_par) > 0):
print('and %d constrained parameter%s' % (len(const_par), 's' if len(const_par) > 1 else ''), const_par)
x_f = np.vectorize(lambda o: o.value)(x) x_f = np.vectorize(lambda o: o.value)(x)
dx_f = np.vectorize(lambda o: o.dvalue)(x) dx_f = np.vectorize(lambda o: o.dvalue)(x)
@ -243,18 +226,12 @@ def total_least_squares(x, y, func, silent=False, **kwargs):
raise Exception('The minimization procedure did not converge.') raise Exception('The minimization procedure did not converge.')
m = x_f.size m = x_f.size
n_parms_aug = n_parms + len(const_par)
def odr_chisquare(p): def odr_chisquare(p):
model = func(p[:n_parms], p[n_parms:].reshape(x_shape)) model = func(p[:n_parms], p[n_parms:].reshape(x_shape))
chisq = anp.sum(((y_f - model) / dy_f) ** 2) + anp.sum(((x_f - p[n_parms:].reshape(x_shape)) / dx_f) ** 2) chisq = anp.sum(((y_f - model) / dy_f) ** 2) + anp.sum(((x_f - p[n_parms:].reshape(x_shape)) / dx_f) ** 2)
return chisq return chisq
def odr_chisquare_aug(p):
model = func_aug(np.concatenate((p[:n_parms_aug], [o.value for o in const_par])), p[n_parms_aug:].reshape(x_shape))
chisq = anp.sum(((y_f - model) / dy_f) ** 2) + anp.sum(((x_f - p[n_parms_aug:].reshape(x_shape)) / dx_f) ** 2)
return chisq
if kwargs.get('expected_chisquare') is True: if kwargs.get('expected_chisquare') is True:
W = np.diag(1 / np.asarray(np.concatenate((dy_f.ravel(), dx_f.ravel())))) W = np.diag(1 / np.asarray(np.concatenate((dy_f.ravel(), dx_f.ravel()))))
@ -281,32 +258,32 @@ def total_least_squares(x, y, func, silent=False, **kwargs):
print('chisquare/expected_chisquare:', print('chisquare/expected_chisquare:',
output.chisquare_by_expected_chisquare) output.chisquare_by_expected_chisquare)
fitp = np.concatenate((out.beta, [o.value for o in const_par])) fitp = out.beta
hess_inv = np.linalg.pinv(jacobian(jacobian(odr_chisquare_aug))(np.concatenate((fitp, out.xplus.ravel())))) hess_inv = np.linalg.pinv(jacobian(jacobian(odr_chisquare))(np.concatenate((fitp, out.xplus.ravel()))))
def odr_chisquare_compact_x(d): def odr_chisquare_compact_x(d):
model = func_aug(d[:n_parms_aug], d[n_parms_aug:n_parms_aug + m].reshape(x_shape)) model = func(d[:n_parms], d[n_parms:n_parms + m].reshape(x_shape))
chisq = anp.sum(((y_f - model) / dy_f) ** 2) + anp.sum(((d[n_parms_aug + m:].reshape(x_shape) - d[n_parms_aug:n_parms_aug + m].reshape(x_shape)) / dx_f) ** 2) chisq = anp.sum(((y_f - model) / dy_f) ** 2) + anp.sum(((d[n_parms + m:].reshape(x_shape) - d[n_parms:n_parms + m].reshape(x_shape)) / dx_f) ** 2)
return chisq return chisq
jac_jac_x = jacobian(jacobian(odr_chisquare_compact_x))(np.concatenate((fitp, out.xplus.ravel(), x_f.ravel()))) 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_aug + m, n_parms_aug + m:] deriv_x = -hess_inv @ jac_jac_x[:n_parms + m, n_parms + m:]
def odr_chisquare_compact_y(d): def odr_chisquare_compact_y(d):
model = func_aug(d[:n_parms_aug], d[n_parms_aug:n_parms_aug + m].reshape(x_shape)) model = func(d[:n_parms], d[n_parms:n_parms + m].reshape(x_shape))
chisq = anp.sum(((d[n_parms_aug + m:] - model) / dy_f) ** 2) + anp.sum(((x_f - d[n_parms_aug:n_parms_aug + m].reshape(x_shape)) / dx_f) ** 2) chisq = anp.sum(((d[n_parms + m:] - model) / dy_f) ** 2) + anp.sum(((x_f - d[n_parms:n_parms + m].reshape(x_shape)) / dx_f) ** 2)
return chisq return chisq
jac_jac_y = jacobian(jacobian(odr_chisquare_compact_y))(np.concatenate((fitp, out.xplus.ravel(), y_f))) 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_aug + m, n_parms_aug + m:] deriv_y = -hess_inv @ jac_jac_y[:n_parms + m, n_parms + m:]
result = [] result = []
for i in range(n_parms): for i in range(n_parms):
result.append(derived_observable(lambda my_var, **kwargs: (my_var[0] + np.finfo(np.float64).eps) / (x.ravel()[0].value + np.finfo(np.float64).eps) * out.beta[i], list(x.ravel()) + list(y), man_grad=list(deriv_x[i]) + list(deriv_y[i]))) result.append(derived_observable(lambda my_var, **kwargs: (my_var[0] + np.finfo(np.float64).eps) / (x.ravel()[0].value + np.finfo(np.float64).eps) * out.beta[i], list(x.ravel()) + list(y), man_grad=list(deriv_x[i]) + list(deriv_y[i])))
output.fit_parameters = result + const_par output.fit_parameters = result
output.odr_chisquare = odr_chisquare(np.concatenate((out.beta, out.xplus.ravel()))) output.odr_chisquare = odr_chisquare(np.concatenate((out.beta, out.xplus.ravel())))
output.dof = x.shape[-1] - n_parms output.dof = x.shape[-1] - n_parms
@ -460,17 +437,6 @@ def _standard_fit(x, y, func, silent=False, **kwargs):
if not callable(func): if not callable(func):
raise TypeError('func has to be a function.') raise TypeError('func has to be a function.')
func_aug = func
if 'const_par' in kwargs:
const_par = kwargs['const_par']
if isinstance(const_par, Obs):
const_par = [const_par]
def func(p, x):
return func_aug(np.concatenate((p, [o.value for o in const_par])), x)
else:
const_par = []
for i in range(25): for i in range(25):
try: try:
func(np.arange(i), x.T[0]) func(np.arange(i), x.T[0])
@ -483,8 +449,6 @@ def _standard_fit(x, y, func, silent=False, **kwargs):
if not silent: if not silent:
print('Fit with', n_parms, 'parameter' + 's' * (n_parms > 1)) print('Fit with', n_parms, 'parameter' + 's' * (n_parms > 1))
if(len(const_par) > 0):
print('and %d constrained parameter%s' % (len(const_par), 's' if len(const_par) > 1 else ''), const_par)
y_f = [o.value for o in y] y_f = [o.value for o in y]
dy_f = [o.dvalue for o in y] dy_f = [o.dvalue for o in y]
@ -517,23 +481,12 @@ def _standard_fit(x, y, func, silent=False, **kwargs):
model = func(p, x) model = func(p, x)
chisq = anp.sum(anp.dot(chol_inv, (y_f - model)) ** 2) chisq = anp.sum(anp.dot(chol_inv, (y_f - model)) ** 2)
return chisq return chisq
def chisqfunc_aug(p):
model = func_aug(np.concatenate((p, [o.value for o in const_par])), x)
chisq = anp.sum(anp.dot(chol_inv, (y_f - model)) ** 2)
return chisq
else: else:
def chisqfunc(p): def chisqfunc(p):
model = func(p, x) model = func(p, x)
chisq = anp.sum(((y_f - model) / dy_f) ** 2) chisq = anp.sum(((y_f - model) / dy_f) ** 2)
return chisq return chisq
def chisqfunc_aug(p):
model = func_aug(np.concatenate((p, [o.value for o in const_par])), x)
chisq = anp.sum(((y_f - model) / dy_f) ** 2)
return chisq
if 'method' in kwargs: if 'method' in kwargs:
output.method = kwargs.get('method') output.method = kwargs.get('method')
if not silent: if not silent:
@ -596,31 +549,30 @@ def _standard_fit(x, y, func, silent=False, **kwargs):
print('chisquare/expected_chisquare:', print('chisquare/expected_chisquare:',
output.chisquare_by_expected_chisquare) output.chisquare_by_expected_chisquare)
fitp = np.concatenate((fit_result.x, [o.value for o in const_par])) fitp = fit_result.x
hess_inv = np.linalg.pinv(jacobian(jacobian(chisqfunc_aug))(fitp)) hess_inv = np.linalg.pinv(jacobian(jacobian(chisqfunc))(fitp))
n_parms_aug = n_parms + len(const_par)
if kwargs.get('correlated_fit') is True: if kwargs.get('correlated_fit') is True:
def chisqfunc_compact(d): def chisqfunc_compact(d):
model = func_aug(d[:n_parms_aug], x) model = func(d[:n_parms], x)
chisq = anp.sum(anp.dot(chol_inv, (d[n_parms_aug:] - model)) ** 2) chisq = anp.sum(anp.dot(chol_inv, (d[n_parms:] - model)) ** 2)
return chisq return chisq
else: else:
def chisqfunc_compact(d): def chisqfunc_compact(d):
model = func_aug(d[:n_parms_aug], x) model = func(d[:n_parms], x)
chisq = anp.sum(((d[n_parms_aug:] - model) / dy_f) ** 2) chisq = anp.sum(((d[n_parms:] - model) / dy_f) ** 2)
return chisq return chisq
jac_jac = jacobian(jacobian(chisqfunc_compact))(np.concatenate((fitp, y_f))) jac_jac = jacobian(jacobian(chisqfunc_compact))(np.concatenate((fitp, y_f)))
deriv = -hess_inv @ jac_jac[:n_parms_aug, n_parms_aug:] deriv = -hess_inv @ jac_jac[:n_parms, n_parms:]
result = [] result = []
for i in range(n_parms): for i in range(n_parms):
result.append(derived_observable(lambda x, **kwargs: (x[0] + np.finfo(np.float64).eps) / (y[0].value + np.finfo(np.float64).eps) * fit_result.x[i], list(y), man_grad=list(deriv[i]))) result.append(derived_observable(lambda x, **kwargs: (x[0] + np.finfo(np.float64).eps) / (y[0].value + np.finfo(np.float64).eps) * fit_result.x[i], list(y), man_grad=list(deriv[i])))
output.fit_parameters = result + const_par output.fit_parameters = result
output.chisquare = chisqfunc(fit_result.x) output.chisquare = chisqfunc(fit_result.x)
output.dof = x.shape[-1] - n_parms output.dof = x.shape[-1] - n_parms