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total_least_squares docstring updated
This commit is contained in:
Fabian Joswig
parent
effccb1cc8
commit
2a594dfb4b
1 changed files with 149 additions and 144 deletions
293
pyerrors/fits.py
293
pyerrors/fits.py
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@ -111,149 +111,8 @@ def least_squares(x, y, func, priors=None, silent=False, **kwargs):
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return _standard_fit(x, y, func, silent=silent, **kwargs)
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return _standard_fit(x, y, func, silent=silent, **kwargs)
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def standard_fit(x, y, func, silent=False, **kwargs):
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warnings.warn("standard_fit renamed to least_squares", DeprecationWarning)
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return least_squares(x, y, func, silent=silent, **kwargs)
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def _standard_fit(x, y, func, silent=False, **kwargs):
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output = Fit_result()
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output.fit_function = func
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x = np.asarray(x)
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if x.shape[-1] != len(y):
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raise Exception('x and y input have to have the same length')
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if len(x.shape) > 2:
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raise Exception('Unkown format for x values')
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if not callable(func):
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raise TypeError('func has to be a function.')
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for i in range(25):
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try:
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func(np.arange(i), x.T[0])
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except:
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pass
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else:
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break
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n_parms = i
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if not silent:
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print('Fit with', n_parms, 'parameters')
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y_f = [o.value for o in y]
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dy_f = [o.dvalue for o in y]
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if np.any(np.asarray(dy_f) <= 0.0):
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raise Exception('No y errors available, run the gamma method first.')
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if 'initial_guess' in kwargs:
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x0 = kwargs.get('initial_guess')
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if len(x0) != n_parms:
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raise Exception('Initial guess does not have the correct length.')
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else:
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x0 = [0.1] * n_parms
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def chisqfunc(p):
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model = func(p, x)
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chisq = anp.sum(((y_f - model) / dy_f) ** 2)
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return chisq
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if 'method' in kwargs:
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output.method = kwargs.get('method')
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if not silent:
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print('Method:', kwargs.get('method'))
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if kwargs.get('method') == 'migrad':
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fit_result = iminuit.minimize(chisqfunc, x0)
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fit_result = iminuit.minimize(chisqfunc, fit_result.x)
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else:
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fit_result = scipy.optimize.minimize(chisqfunc, x0, method=kwargs.get('method'))
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fit_result = scipy.optimize.minimize(chisqfunc, fit_result.x, method=kwargs.get('method'), tol=1e-12)
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chisquare = fit_result.fun
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output.iterations = fit_result.nit
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else:
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output.method = 'Levenberg-Marquardt'
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if not silent:
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print('Method: Levenberg-Marquardt')
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def chisqfunc_residuals(p):
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model = func(p, x)
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chisq = ((y_f - model) / dy_f)
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return chisq
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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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chisquare = np.sum(fit_result.fun ** 2)
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output.iterations = fit_result.nfev
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if not fit_result.success:
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raise Exception('The minimization procedure did not converge.')
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if x.shape[-1] - n_parms > 0:
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output.chisquare_by_dof = chisquare / (x.shape[-1] - n_parms)
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else:
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output.chisquare_by_dof = float('nan')
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output.message = fit_result.message
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if not silent:
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print(fit_result.message)
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print('chisquare/d.o.f.:', output.chisquare_by_dof)
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if kwargs.get('expected_chisquare') is True:
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W = np.diag(1 / np.asarray(dy_f))
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cov = covariance_matrix(y)
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A = W @ jacobian(func)(fit_result.x, x)
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P_phi = A @ np.linalg.inv(A.T @ A) @ A.T
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expected_chisquare = np.trace((np.identity(x.shape[-1]) - P_phi) @ W @ cov @ W)
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output.chisquare_by_expected_chisquare = chisquare / expected_chisquare
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if not silent:
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print('chisquare/expected_chisquare:',
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output.chisquare_by_expected_chisquare)
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hess_inv = np.linalg.pinv(jacobian(jacobian(chisqfunc))(fit_result.x))
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def chisqfunc_compact(d):
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model = func(d[:n_parms], x)
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chisq = anp.sum(((d[n_parms:] - model) / dy_f) ** 2)
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return chisq
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jac_jac = jacobian(jacobian(chisqfunc_compact))(np.concatenate((fit_result.x, y_f)))
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deriv = -hess_inv @ jac_jac[:n_parms, n_parms:]
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result = []
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for i in range(n_parms):
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result.append(derived_observable(lambda x, **kwargs: x[0], [pseudo_Obs(fit_result.x[i], 0.0, y[0].names[0], y[0].shape[y[0].names[0]])] + list(y), man_grad=[0] + list(deriv[i])))
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output.fit_parameters = result
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output.chisquare = chisqfunc(fit_result.x)
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output.dof = x.shape[-1] - n_parms
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if kwargs.get('resplot') is True:
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residual_plot(x, y, func, result)
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if kwargs.get('qqplot') is True:
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qqplot(x, y, func, result)
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return output
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def odr_fit(x, y, func, silent=False, **kwargs):
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warnings.warn("odr_fit renamed to total_least_squares", DeprecationWarning)
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return total_least_squares(x, y, func, silent=silent, **kwargs)
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def total_least_squares(x, y, func, silent=False, **kwargs):
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def total_least_squares(x, y, func, silent=False, **kwargs):
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"""Performs a non-linear fit to y = func(x) and returns a list of Obs corresponding to the fit parameters.
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r'''Performs a non-linear fit to y = func(x) and returns a list of Obs corresponding to the fit parameters.
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Parameters
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Parameters
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----------
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----------
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@ -264,21 +123,24 @@ def total_least_squares(x, y, func, silent=False, **kwargs):
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func : object
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func : object
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func has to be of the form
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func has to be of the form
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```python
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def func(a, x):
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def func(a, x):
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y = a[0] + a[1] * x + a[2] * anp.sinh(x)
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y = a[0] + a[1] * x + a[2] * anp.sinh(x)
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return y
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return y
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```
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For multiple x values func can be of the form
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For multiple x values func can be of the form
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```python
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def func(a, x):
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def func(a, x):
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(x1, x2) = x
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(x1, x2) = x
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return a[0] * x1 ** 2 + a[1] * x2
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return a[0] * x1 ** 2 + a[1] * x2
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```
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It is important that all numpy functions refer to autograd.numpy, otherwise the differentiation
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It is important that all numpy functions refer to autograd.numpy, otherwise the differentiation
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will not work.
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will not work.
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silent : bool, optional
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silent : bool, optional
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If true all output to the console is omitted (default False).
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If true all output to the console is omitted (default False).
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Based on the orthogonal distance regression module of scipy
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initial_guess : list
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initial_guess : list
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can provide an initial guess for the input parameters. Relevant for non-linear
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can provide an initial guess for the input parameters. Relevant for non-linear
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fits with many parameters.
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fits with many parameters.
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@ -287,7 +149,9 @@ def total_least_squares(x, y, func, silent=False, **kwargs):
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corrected by effects caused by correlated input data.
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corrected by effects caused by correlated input data.
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This can take a while as the full correlation matrix
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This can take a while as the full correlation matrix
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has to be calculated (default False).
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has to be calculated (default False).
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"""
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Based on the orthogonal distance regression module of scipy
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'''
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output = Fit_result()
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output = Fit_result()
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@ -541,6 +405,147 @@ def _prior_fit(x, y, func, priors, silent=False, **kwargs):
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return output
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return output
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def standard_fit(x, y, func, silent=False, **kwargs):
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warnings.warn("standard_fit renamed to least_squares", DeprecationWarning)
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return least_squares(x, y, func, silent=silent, **kwargs)
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def _standard_fit(x, y, func, silent=False, **kwargs):
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output = Fit_result()
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output.fit_function = func
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x = np.asarray(x)
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if x.shape[-1] != len(y):
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raise Exception('x and y input have to have the same length')
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if len(x.shape) > 2:
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raise Exception('Unkown format for x values')
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if not callable(func):
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raise TypeError('func has to be a function.')
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for i in range(25):
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try:
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func(np.arange(i), x.T[0])
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except:
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pass
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else:
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break
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n_parms = i
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if not silent:
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print('Fit with', n_parms, 'parameters')
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y_f = [o.value for o in y]
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dy_f = [o.dvalue for o in y]
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if np.any(np.asarray(dy_f) <= 0.0):
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raise Exception('No y errors available, run the gamma method first.')
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if 'initial_guess' in kwargs:
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x0 = kwargs.get('initial_guess')
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if len(x0) != n_parms:
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raise Exception('Initial guess does not have the correct length.')
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else:
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x0 = [0.1] * n_parms
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def chisqfunc(p):
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model = func(p, x)
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chisq = anp.sum(((y_f - model) / dy_f) ** 2)
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return chisq
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if 'method' in kwargs:
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output.method = kwargs.get('method')
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if not silent:
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print('Method:', kwargs.get('method'))
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if kwargs.get('method') == 'migrad':
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fit_result = iminuit.minimize(chisqfunc, x0)
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fit_result = iminuit.minimize(chisqfunc, fit_result.x)
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else:
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fit_result = scipy.optimize.minimize(chisqfunc, x0, method=kwargs.get('method'))
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fit_result = scipy.optimize.minimize(chisqfunc, fit_result.x, method=kwargs.get('method'), tol=1e-12)
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chisquare = fit_result.fun
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output.iterations = fit_result.nit
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else:
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output.method = 'Levenberg-Marquardt'
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if not silent:
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print('Method: Levenberg-Marquardt')
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def chisqfunc_residuals(p):
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model = func(p, x)
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chisq = ((y_f - model) / dy_f)
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return chisq
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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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chisquare = np.sum(fit_result.fun ** 2)
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output.iterations = fit_result.nfev
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if not fit_result.success:
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raise Exception('The minimization procedure did not converge.')
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if x.shape[-1] - n_parms > 0:
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output.chisquare_by_dof = chisquare / (x.shape[-1] - n_parms)
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else:
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output.chisquare_by_dof = float('nan')
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output.message = fit_result.message
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if not silent:
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print(fit_result.message)
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print('chisquare/d.o.f.:', output.chisquare_by_dof)
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if kwargs.get('expected_chisquare') is True:
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W = np.diag(1 / np.asarray(dy_f))
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cov = covariance_matrix(y)
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A = W @ jacobian(func)(fit_result.x, x)
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P_phi = A @ np.linalg.inv(A.T @ A) @ A.T
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expected_chisquare = np.trace((np.identity(x.shape[-1]) - P_phi) @ W @ cov @ W)
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output.chisquare_by_expected_chisquare = chisquare / expected_chisquare
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if not silent:
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print('chisquare/expected_chisquare:',
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output.chisquare_by_expected_chisquare)
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hess_inv = np.linalg.pinv(jacobian(jacobian(chisqfunc))(fit_result.x))
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def chisqfunc_compact(d):
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model = func(d[:n_parms], x)
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chisq = anp.sum(((d[n_parms:] - model) / dy_f) ** 2)
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return chisq
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jac_jac = jacobian(jacobian(chisqfunc_compact))(np.concatenate((fit_result.x, y_f)))
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deriv = -hess_inv @ jac_jac[:n_parms, n_parms:]
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result = []
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for i in range(n_parms):
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result.append(derived_observable(lambda x, **kwargs: x[0], [pseudo_Obs(fit_result.x[i], 0.0, y[0].names[0], y[0].shape[y[0].names[0]])] + list(y), man_grad=[0] + list(deriv[i])))
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output.fit_parameters = result
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output.chisquare = chisqfunc(fit_result.x)
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output.dof = x.shape[-1] - n_parms
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if kwargs.get('resplot') is True:
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residual_plot(x, y, func, result)
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if kwargs.get('qqplot') is True:
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qqplot(x, y, func, result)
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return output
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def odr_fit(x, y, func, silent=False, **kwargs):
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warnings.warn("odr_fit renamed to total_least_squares", DeprecationWarning)
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return total_least_squares(x, y, func, silent=silent, **kwargs)
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def fit_lin(x, y, **kwargs):
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def fit_lin(x, y, **kwargs):
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"""Performs a linear fit to y = n + m * x and returns two Obs n, m.
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"""Performs a linear fit to y = n + m * x and returns two Obs n, m.
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