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https://github.com/fjosw/pyerrors.git
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[Fix] Migrate to odrpack because of scipy.odr deprecation in recent
release
This commit is contained in:
Fabian Joswig
parent
da399b7c02
commit
46d32683d3
3 changed files with 80 additions and 38 deletions
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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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@ -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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@ -652,14 +662,14 @@ 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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if not out.success:
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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 +689,9 @@ 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 +700,14 @@ 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 +716,7 @@ 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 +729,7 @@ 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 +743,7 @@ 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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2
setup.py
2
setup.py
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@ -25,7 +25,7 @@ setup(name='pyerrors',
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license="MIT",
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packages=find_packages(),
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python_requires='>=3.10.0',
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install_requires=['numpy>=2.0', 'autograd>=1.7.0', 'numdifftools>=0.9.41', 'matplotlib>=3.9', 'scipy>=1.13', 'iminuit>=2.28', 'h5py>=3.11', 'lxml>=5.0', 'python-rapidjson>=1.20', 'pandas>=2.2'],
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install_requires=['numpy>=2.0', 'autograd>=1.7.0', 'numdifftools>=0.9.41', 'matplotlib>=3.9', 'scipy>=1.13', 'iminuit>=2.28', 'h5py>=3.11', 'lxml>=5.0', 'python-rapidjson>=1.20', 'pandas>=2.2', 'odrpack>=0.4'],
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extras_require={'test': ['pytest', 'pytest-cov', 'pytest-benchmark', 'hypothesis', 'nbmake', 'flake8']},
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classifiers=[
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'Development Status :: 5 - Production/Stable',
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@ -3,7 +3,7 @@ import autograd.numpy as anp
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import matplotlib.pyplot as plt
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import math
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import scipy.optimize
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from scipy.odr import ODR, Model, RealData
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from odrpack import odr_fit
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from scipy.linalg import cholesky
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from scipy.stats import norm
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import iminuit
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@ -397,11 +397,21 @@ def test_total_least_squares():
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y = a[0] * anp.exp(-a[1] * x)
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return y
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data = RealData([o.value for o in ox], [o.value for o in oy], sx=[o.dvalue for o in ox], sy=[o.dvalue for o in oy])
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model = Model(func)
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odr = ODR(data, model, [0, 0], partol=np.finfo(np.float64).eps)
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odr.set_job(fit_type=0, deriv=1)
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output = 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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output = odr_fit(
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wrapped_func,
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np.array([o.value for o in ox]),
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np.array([o.value for o in oy]),
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beta0=np.array([0.0, 0.0]),
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weight_x=1.0 / np.array([o.dvalue for o in ox]) ** 2,
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weight_y=1.0 / np.array([o.dvalue for o in oy]) ** 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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out = pe.total_least_squares(ox, oy, func, expected_chisquare=True)
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beta = out.fit_parameters
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@ -1431,11 +1441,11 @@ def fit_general(x, y, func, silent=False, **kwargs):
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global print_output, beta0
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print_output = 1
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if 'initial_guess' in kwargs:
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beta0 = kwargs.get('initial_guess')
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beta0 = np.asarray(kwargs.get('initial_guess'), dtype=np.float64)
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if len(beta0) != 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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beta0 = np.arange(n_parms)
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beta0 = np.arange(n_parms, dtype=np.float64)
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if len(x) != len(y):
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raise Exception('x and y have to have the same length')
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@ -1463,23 +1473,45 @@ def fit_general(x, y, func, silent=False, **kwargs):
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xerr = kwargs.get('xerr')
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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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if length == len(obs):
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assert 'x_constants' in kwargs
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data = RealData(kwargs.get('x_constants'), obs, sy=yerr)
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fit_type = 2
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x_data = np.asarray(kwargs.get('x_constants'))
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y_data = np.asarray(obs)
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# Ordinary least squares (no x errors)
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output = odr_fit(
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wrapped_func,
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x_data,
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y_data,
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beta0=beta0,
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weight_y=1.0 / np.asarray(yerr) ** 2,
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partol=np.finfo(np.float64).eps,
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task='explicit-OLS',
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diff_scheme='central'
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)
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elif length == len(obs) // 2:
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data = RealData(obs[:length], obs[length:], sx=xerr, sy=yerr)
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fit_type = 0
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x_data = np.asarray(obs[:length])
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y_data = np.asarray(obs[length:])
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# ODR with x errors
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output = odr_fit(
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wrapped_func,
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x_data,
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y_data,
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beta0=beta0,
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weight_x=1.0 / np.asarray(xerr) ** 2,
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weight_y=1.0 / np.asarray(yerr) ** 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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else:
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raise Exception('x and y do not fit together.')
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model = Model(func)
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odr = ODR(data, model, beta0, partol=np.finfo(np.float64).eps)
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odr.set_job(fit_type=fit_type, deriv=1)
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output = odr.run()
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if print_output and not silent:
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print(*output.stopreason)
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print(output.stopreason)
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print('chisquare/d.o.f.:', output.res_var)
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print_output = 0
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beta0 = output.beta
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