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fit_result class added
This commit is contained in:
Fabian Joswig
parent
a5fec92e1e
commit
4ceb35992e
1 changed files with 46 additions and 41 deletions
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@ -1,6 +1,3 @@
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#!/usr/bin/env python
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# coding: utf-8
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import gc
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import warnings
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import numpy as np
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@ -16,6 +13,13 @@ from autograd import elementwise_grad as egrad
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from .pyerrors import Obs, derived_observable, covariance, pseudo_Obs
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class Fit_result:
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def __init__(self):
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self.fit_parameters = None
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def standard_fit(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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@ -38,8 +42,6 @@ def standard_fit(x, y, func, silent=False, **kwargs):
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Keyword arguments
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-----------------
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dict_output -- If true, the output is a dictionary containing all relevant
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data instead of just a list of the fit parameters.
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silent -- If true all output to the console is omitted (default False).
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initial_guess -- can provide an initial guess for the input parameters. Relevant for
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non-linear fits with many parameters.
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@ -55,9 +57,10 @@ def standard_fit(x, y, func, silent=False, **kwargs):
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has to be calculated (default False).
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"""
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result_dict = {}
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result_dict['fit_function'] = func
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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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@ -102,7 +105,7 @@ def standard_fit(x, y, func, silent=False, **kwargs):
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return chisq
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if 'method' in kwargs:
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result_dict['method'] = kwargs.get('method')
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utput.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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@ -114,7 +117,7 @@ def standard_fit(x, y, func, silent=False, **kwargs):
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chisquare = fit_result.fun
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else:
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result_dict['method'] = 'Levenberg-Marquardt'
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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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@ -131,13 +134,13 @@ def standard_fit(x, y, func, silent=False, **kwargs):
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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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result_dict['chisquare/d.o.f.'] = chisquare / (x.shape[-1] - n_parms)
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output.chisquare_by_dof = chisquare / (x.shape[-1] - n_parms)
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else:
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result_dict['chisquare/d.o.f.'] = float('nan')
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output.chisquare_by_dof = float('nan')
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if not silent:
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print(fit_result.message)
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print('chisquare/d.o.f.:', result_dict['chisquare/d.o.f.'])
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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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@ -145,10 +148,10 @@ def standard_fit(x, y, func, silent=False, **kwargs):
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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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result_dict['chisquare/expected_chisquare'] = chisquare / expected_chisquare
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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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result_dict['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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@ -165,10 +168,10 @@ def standard_fit(x, y, func, silent=False, **kwargs):
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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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result_dict['fit_parameters'] = result
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output.fit_parameters = result
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result_dict['chisquare'] = chisqfunc(fit_result.x)
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result_dict['d.o.f.'] = x.shape[-1] - n_parms
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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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@ -176,7 +179,7 @@ def standard_fit(x, y, func, silent=False, **kwargs):
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if kwargs.get('qqplot') is True:
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qqplot(x, y, func, result)
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return result_dict if kwargs.get('dict_output') else result
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return output
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def odr_fit(x, y, func, silent=False, **kwargs):
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@ -215,9 +218,9 @@ def odr_fit(x, y, func, silent=False, **kwargs):
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has to be calculated (default False).
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"""
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result_dict = {}
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output = Fit_result()
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result_dict['fit_function'] = func
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output.fit_function = func
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x = np.array(x)
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@ -262,16 +265,16 @@ def odr_fit(x, y, func, silent=False, **kwargs):
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odr.set_job(fit_type=0, deriv=1)
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output = odr.run()
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result_dict['residual_variance'] = output.res_var
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output.residual_variance = output.res_var
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result_dict['method'] = 'ODR'
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output.method = 'ODR'
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result_dict['xplus'] = output.xplus
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output.xplus = output.xplus
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if not silent:
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print('Method: ODR')
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print(*output.stopreason)
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print('Residual variance:', result_dict['residual_variance'])
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print('Residual variance:', output.residual_variance)
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if output.info > 3:
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raise Exception('The minimization procedure did not converge.')
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@ -304,10 +307,10 @@ def odr_fit(x, y, func, silent=False, **kwargs):
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if expected_chisquare <= 0.0:
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warnings.warn("Negative expected_chisquare.", RuntimeWarning)
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expected_chisquare = np.abs(expected_chisquare)
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result_dict['chisquare/expected_chisquare'] = odr_chisquare(np.concatenate((output.beta, output.xplus.ravel()))) / expected_chisquare
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output.chisquare_by_expected_chisquare = odr_chisquare(np.concatenate((output.beta, output.xplus.ravel()))) / expected_chisquare
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if not silent:
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print('chisquare/expected_chisquare:',
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result_dict['chisquare/expected_chisquare'])
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output.chisquare_by_expected_chisquare)
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hess_inv = np.linalg.pinv(jacobian(jacobian(odr_chisquare))(np.concatenate((output.beta, output.xplus.ravel()))))
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@ -333,12 +336,12 @@ def odr_fit(x, y, func, silent=False, **kwargs):
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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(output.beta[i], 0.0, y[0].names[0], y[0].shape[y[0].names[0]])] + list(x.ravel()) + list(y), man_grad=[0] + list(deriv_x[i]) + list(deriv_y[i])))
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result_dict['fit_parameters'] = result
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output.fit_parameters = result
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result_dict['odr_chisquare'] = odr_chisquare(np.concatenate((output.beta, output.xplus.ravel())))
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result_dict['d.o.f.'] = x.shape[-1] - n_parms
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output.odr_chisquare = odr_chisquare(np.concatenate((output.beta, output.xplus.ravel())))
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output.dof = x.shape[-1] - n_parms
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return result_dict if kwargs.get('dict_output') else result
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return output
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def prior_fit(x, y, func, priors, silent=False, **kwargs):
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@ -375,9 +378,9 @@ def prior_fit(x, y, func, priors, silent=False, **kwargs):
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tol -- Specify the tolerance of the migrad solver (default 1e-4)
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"""
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result_dict = {}
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output = Fit_result()
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result_dict['fit_function'] = func
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output.fit_function = func
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if Obs.e_tag_global < 4:
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warnings.warn("e_tag_global is smaller than 4, this can cause problems when calculating errors from fits with priors", RuntimeWarning)
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@ -416,7 +419,7 @@ def prior_fit(x, y, func, priors, silent=False, **kwargs):
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loc_val, loc_dval = extract_val_and_dval(i_prior)
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loc_priors.append(pseudo_Obs(loc_val, loc_dval, 'p' + str(i_n)))
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result_dict['priors'] = loc_priors
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output.priors = loc_priors
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if not silent:
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print('Fit with', n_parms, 'parameters')
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@ -456,12 +459,12 @@ def prior_fit(x, y, func, priors, silent=False, **kwargs):
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m.migrad()
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params = np.asarray(m.values.values())
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result_dict['chisquare/d.o.f.'] = m.fval / len(x)
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output.chisquare_by_dof = m.fval / len(x)
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result_dict['method'] = 'migrad'
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output.method = 'migrad'
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if not silent:
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print('chisquare/d.o.f.:', result_dict['chisquare/d.o.f.'])
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print('chisquare/d.o.f.:', output.chisquare_by_dof)
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if not m.get_fmin().is_valid:
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raise Exception('The minimization procedure did not converge.')
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@ -481,8 +484,8 @@ def prior_fit(x, y, func, priors, silent=False, **kwargs):
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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(params[i], 0.0, y[0].names[0], y[0].shape[y[0].names[0]])] + list(y) + list(loc_priors), man_grad=[0] + list(deriv[i])))
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result_dict['fit_parameters'] = result
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result_dict['chisquare'] = chisqfunc(np.asarray(params))
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output.fit_parameters = result
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output.chisquare = chisqfunc(np.asarray(params))
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if kwargs.get('resplot') is True:
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residual_plot(x, y, func, result)
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@ -490,7 +493,7 @@ def prior_fit(x, y, func, priors, silent=False, **kwargs):
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if kwargs.get('qqplot') is True:
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qqplot(x, y, func, result)
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return result_dict if kwargs.get('dict_output') else result
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return output
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def fit_lin(x, y, **kwargs):
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@ -506,9 +509,11 @@ def fit_lin(x, y, **kwargs):
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return y
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if all(isinstance(n, Obs) for n in x):
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return odr_fit(x, y, f, **kwargs)
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out = odr_fit(x, y, f, **kwargs)
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return out.fit_parameters
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elif all(isinstance(n, float) or isinstance(n, int) for n in x) or isinstance(x, np.ndarray):
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return standard_fit(x, y, f, **kwargs)
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out = standard_fit(x, y, f, **kwargs)
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return out.fit_parameters
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else:
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raise Exception('Unsupported types for x')
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