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[Fix] Migrate to odrpack because of scipy.odr deprecation (#279)
* [Fix] Migrate to odrpack because of scipy.odr deprecation in recent release * [Fix] Fix behaviour for rank deficient fits. Add test * [Fix] Relax test_merge_obs tolerance to machine epsilon and update ODR docstring to reference odrpack * [ci] Re-add -Werror to pytest workflow * [Fix] Handle platform-dependent rank-deficient warning in ODR tests * [Fix] Improve rank-deficient detection and bump odrpack to >=0.5 Fix incorrect ODRPACK95 info code parsing: rank deficiency is encoded in the tens digit (info // 10 % 10), not the hundreds digit. Add irank and inv_condnum to the warning message for diagnostics.
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Fabian Joswig
GitHub
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commit
b28c2f0b6f
6 changed files with 119 additions and 44 deletions
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@ -394,7 +394,7 @@ Direct visualizations of the performed fits can be triggered via `resplot=True`
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For all available options including combined fits to multiple datasets see `pyerrors.fits.least_squares`.
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## Total least squares fits
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`pyerrors` can also fit data with errors on both the dependent and independent variables using the total least squares method also referred to as orthogonal distance regression as implemented in [scipy](https://docs.scipy.org/doc/scipy/reference/odr.html), see `pyerrors.fits.least_squares`. The syntax is identical to the standard least squares case, the only difference being that `x` also has to be a `list` or `numpy.array` of `Obs`.
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`pyerrors` can also fit data with errors on both the dependent and independent variables using the total least squares method also referred to as orthogonal distance regression as implemented in [odrpack](https://pypi.org/project/odrpack/), see `pyerrors.fits.total_least_squares`. The syntax is identical to the standard least squares case, the only difference being that `x` also has to be a `list` or `numpy.array` of `Obs`.
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For the full API see `pyerrors.fits` for fits and `pyerrors.roots` for finding roots of functions.
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@ -7,7 +7,7 @@ import scipy.optimize
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import scipy.stats
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import matplotlib.pyplot as plt
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from matplotlib import gridspec
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from scipy.odr import ODR, Model, RealData
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from odrpack import odr_fit
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import iminuit
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from autograd import jacobian as auto_jacobian
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from autograd import hessian as auto_hessian
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@ -567,7 +567,7 @@ def total_least_squares(x, y, func, silent=False, **kwargs):
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Notes
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-----
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Based on the orthogonal distance regression module of scipy.
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Based on the odrpack orthogonal distance regression library.
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Returns
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-------
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@ -634,17 +634,27 @@ def total_least_squares(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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if 'initial_guess' in kwargs:
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x0 = kwargs.get('initial_guess')
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x0 = np.asarray(kwargs.get('initial_guess'), dtype=np.float64)
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if len(x0) != n_parms:
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raise Exception('Initial guess does not have the correct length: %d vs. %d' % (len(x0), n_parms))
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else:
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x0 = [1] * n_parms
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x0 = np.ones(n_parms, dtype=np.float64)
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data = RealData(x_f, y_f, sx=dx_f, sy=dy_f)
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model = Model(func)
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odr = ODR(data, model, x0, partol=np.finfo(np.float64).eps)
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odr.set_job(fit_type=0, deriv=1)
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out = odr.run()
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# odrpack expects f(x, beta), but pyerrors convention is f(beta, x)
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def wrapped_func(x, beta):
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return func(beta, x)
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out = odr_fit(
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wrapped_func,
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np.asarray(x_f, dtype=np.float64),
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np.asarray(y_f, dtype=np.float64),
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beta0=x0,
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weight_x=1.0 / np.asarray(dx_f, dtype=np.float64) ** 2,
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weight_y=1.0 / np.asarray(dy_f, dtype=np.float64) ** 2,
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partol=np.finfo(np.float64).eps,
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task='explicit-ODR',
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diff_scheme='central'
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)
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output.residual_variance = out.res_var
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@ -652,15 +662,29 @@ def total_least_squares(x, y, func, silent=False, **kwargs):
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output.message = out.stopreason
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output.xplus = out.xplus
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output.xplus = out.xplusd
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if not silent:
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print('Method: ODR')
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print(*out.stopreason)
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print(out.stopreason)
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print('Residual variance:', output.residual_variance)
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if out.info > 3:
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raise Exception('The minimization procedure did not converge.')
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if not out.success:
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# ODRPACK95 info code structure (see User Guide §4):
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# info % 10 -> convergence: 1=sum-of-sq, 2=param, 3=both
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# info // 10 % 10 -> 1 = problem not full rank at solution
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convergence_status = out.info % 10
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rank_deficient = (out.info // 10 % 10) == 1
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if convergence_status in [1, 2, 3] and rank_deficient:
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warnings.warn(
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f"ODR fit is rank deficient (irank={out.irank}, inv_condnum={out.inv_condnum:.2e}). "
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"This may indicate a vanishing chi-squared (n_obs == n_parms). "
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"Results may be unreliable.",
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RuntimeWarning
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)
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else:
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raise Exception('The minimization procedure did not converge.')
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m = x_f.size
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@ -679,9 +703,9 @@ def total_least_squares(x, y, func, silent=False, **kwargs):
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number_of_x_parameters = int(m / x_f.shape[-1])
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old_jac = jacobian(func)(out.beta, out.xplus)
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old_jac = jacobian(func)(out.beta, out.xplusd)
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fused_row1 = np.concatenate((old_jac, np.concatenate((number_of_x_parameters * [np.zeros(old_jac.shape)]), axis=0)))
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fused_row2 = np.concatenate((jacobian(lambda x, y: func(y, x))(out.xplus, out.beta).reshape(x_f.shape[-1], x_f.shape[-1] * number_of_x_parameters), np.identity(number_of_x_parameters * old_jac.shape[0])))
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fused_row2 = np.concatenate((jacobian(lambda x, y: func(y, x))(out.xplusd, out.beta).reshape(x_f.shape[-1], x_f.shape[-1] * number_of_x_parameters), np.identity(number_of_x_parameters * old_jac.shape[0])))
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new_jac = np.concatenate((fused_row1, fused_row2), axis=1)
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A = W @ new_jac
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@ -690,14 +714,14 @@ def total_least_squares(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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output.chisquare_by_expected_chisquare = odr_chisquare(np.concatenate((out.beta, out.xplus.ravel()))) / expected_chisquare
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output.chisquare_by_expected_chisquare = odr_chisquare(np.concatenate((out.beta, out.xplusd.ravel()))) / 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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fitp = out.beta
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try:
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hess = hessian(odr_chisquare)(np.concatenate((fitp, out.xplus.ravel())))
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hess = hessian(odr_chisquare)(np.concatenate((fitp, out.xplusd.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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@ -706,7 +730,7 @@ def total_least_squares(x, y, func, silent=False, **kwargs):
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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)
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return chisq
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jac_jac_x = hessian(odr_chisquare_compact_x)(np.concatenate((fitp, out.xplus.ravel(), x_f.ravel())))
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jac_jac_x = hessian(odr_chisquare_compact_x)(np.concatenate((fitp, out.xplusd.ravel(), x_f.ravel())))
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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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@ -719,7 +743,7 @@ def total_least_squares(x, y, func, silent=False, **kwargs):
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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)
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return chisq
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jac_jac_y = hessian(odr_chisquare_compact_y)(np.concatenate((fitp, out.xplus.ravel(), y_f)))
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jac_jac_y = hessian(odr_chisquare_compact_y)(np.concatenate((fitp, out.xplusd.ravel(), y_f)))
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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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@ -733,7 +757,7 @@ def total_least_squares(x, y, func, silent=False, **kwargs):
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output.fit_parameters = result
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output.odr_chisquare = odr_chisquare(np.concatenate((out.beta, out.xplus.ravel())))
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output.odr_chisquare = odr_chisquare(np.concatenate((out.beta, out.xplusd.ravel())))
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output.dof = x.shape[-1] - n_parms
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output.p_value = 1 - scipy.stats.chi2.cdf(output.odr_chisquare, output.dof)
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