---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
<ipython-input-33-9f97797a4d66> in <module>
----> 1 crosscheck_bins.build_emulators(hide_progress=False,fit_model="polynomial")
2 crosscheck.build_emulators(hide_progress=False,fit_model="linear")
/opt/anaconda3/lib/python3.7/site-packages/swiftemulator/sensitivity/cross_check_bins.py in build_emulators(self, kernel, fit_model, lasso_model_alpha, polynomial_degree, hide_progress)
119 fit_model=fit_model,
120 lasso_model_alpha=lasso_model_alpha,
--> 121 polynomial_degree=polynomial_degree,
122 )
123
/opt/anaconda3/lib/python3.7/site-packages/swiftemulator/emulators/gaussian_process_bins.py in fit_model(self, kernel, fit_model, lasso_model_alpha, polynomial_degree)
261 fun=negative_log_likelihood,
262 x0=gaussian_process.get_parameter_vector(),
--> 263 jac=grad_negative_log_likelihood,
264 )
265
/opt/anaconda3/lib/python3.7/site-packages/scipy/optimize/_minimize.py in minimize(fun, x0, args, method, jac, hess, hessp, bounds, constraints, tol, callback, options)
610 return _minimize_cg(fun, x0, args, jac, callback, **options)
611 elif meth == 'bfgs':
--> 612 return _minimize_bfgs(fun, x0, args, jac, callback, **options)
613 elif meth == 'newton-cg':
614 return _minimize_newtoncg(fun, x0, args, jac, hess, hessp, callback,
/opt/anaconda3/lib/python3.7/site-packages/scipy/optimize/optimize.py in _minimize_bfgs(fun, x0, args, jac, callback, gtol, norm, eps, maxiter, disp, return_all, finite_diff_rel_step, **unknown_options)
1134 alpha_k, fc, gc, old_fval, old_old_fval, gfkp1 = \
1135 _line_search_wolfe12(f, myfprime, xk, pk, gfk,
-> 1136 old_fval, old_old_fval, amin=1e-100, amax=1e100)
1137 except _LineSearchError:
1138 # Line search failed to find a better solution.
/opt/anaconda3/lib/python3.7/site-packages/scipy/optimize/optimize.py in _line_search_wolfe12(f, fprime, xk, pk, gfk, old_fval, old_old_fval, **kwargs)
934 ret = line_search_wolfe1(f, fprime, xk, pk, gfk,
935 old_fval, old_old_fval,
--> 936 **kwargs)
937
938 if ret[0] is not None and extra_condition is not None:
/opt/anaconda3/lib/python3.7/site-packages/scipy/optimize/linesearch.py in line_search_wolfe1(f, fprime, xk, pk, gfk, old_fval, old_old_fval, args, c1, c2, amax, amin, xtol)
96 stp, fval, old_fval = scalar_search_wolfe1(
97 phi, derphi, old_fval, old_old_fval, derphi0,
---> 98 c1=c1, c2=c2, amax=amax, amin=amin, xtol=xtol)
99
100 return stp, fc[0], gc[0], fval, old_fval, gval[0]
/opt/anaconda3/lib/python3.7/site-packages/scipy/optimize/linesearch.py in scalar_search_wolfe1(phi, derphi, phi0, old_phi0, derphi0, c1, c2, amax, amin, xtol)
170 if task[:2] == b'FG':
171 alpha1 = stp
--> 172 phi1 = phi(stp)
173 derphi1 = derphi(stp)
174 else:
/opt/anaconda3/lib/python3.7/site-packages/scipy/optimize/linesearch.py in phi(s)
82 def phi(s):
83 fc[0] += 1
---> 84 return f(xk + s*pk, *args)
85
86 def derphi(s):
/opt/anaconda3/lib/python3.7/site-packages/scipy/optimize/_differentiable_functions.py in fun(self, x)
180 if not np.array_equal(x, self.x):
181 self._update_x_impl(x)
--> 182 self._update_fun()
183 return self.f
184
/opt/anaconda3/lib/python3.7/site-packages/scipy/optimize/_differentiable_functions.py in _update_fun(self)
164 def _update_fun(self):
165 if not self.f_updated:
--> 166 self._update_fun_impl()
167 self.f_updated = True
168
/opt/anaconda3/lib/python3.7/site-packages/scipy/optimize/_differentiable_functions.py in update_fun()
71
72 def update_fun():
---> 73 self.f = fun_wrapped(self.x)
74
75 self._update_fun_impl = update_fun
/opt/anaconda3/lib/python3.7/site-packages/scipy/optimize/_differentiable_functions.py in fun_wrapped(x)
68 def fun_wrapped(x):
69 self.nfev += 1
---> 70 return fun(x, *args)
71
72 def update_fun():
/opt/anaconda3/lib/python3.7/site-packages/swiftemulator/emulators/gaussian_process_bins.py in negative_log_likelihood(p)
251 def negative_log_likelihood(p):
252 gaussian_process.set_parameter_vector(p)
--> 253 return -gaussian_process.log_likelihood(dependent_variables)
254
255 def grad_negative_log_likelihood(p):
/opt/anaconda3/lib/python3.7/site-packages/george/gp.py in log_likelihood(self, y, quiet)
358
359 """
--> 360 if not self.recompute(quiet=quiet):
361 return -np.inf
362 try:
/opt/anaconda3/lib/python3.7/site-packages/george/gp.py in recompute(self, quiet, **kwargs)
330 # Update the model making sure that we store the original
331 # ordering of the points.
--> 332 self.compute(self._x, np.sqrt(self._yerr2), **kwargs)
333 except (ValueError, LinAlgError):
334 if quiet:
/opt/anaconda3/lib/python3.7/site-packages/george/gp.py in compute(self, x, yerr, **kwargs)
307 # Include the white noise term.
308 yerr = np.sqrt(self._yerr2 + np.exp(self._call_white_noise(self._x)))
--> 309 self.solver.compute(self._x, yerr, **kwargs)
310
311 self._const = -0.5 * (len(self._x) * np.log(2 * np.pi) +
/opt/anaconda3/lib/python3.7/site-packages/george/solvers/basic.py in compute(self, x, yerr)
66
67 # Factor the matrix and compute the log-determinant.
---> 68 self._factor = (cholesky(K, overwrite_a=True, lower=False), False)
69 self.log_determinant = 2 * np.sum(np.log(np.diag(self._factor[0])))
70 self.computed = True
/opt/anaconda3/lib/python3.7/site-packages/scipy/linalg/decomp_cholesky.py in cholesky(a, lower, overwrite_a, check_finite)
87 """
88 c, lower = _cholesky(a, lower=lower, overwrite_a=overwrite_a, clean=True,
---> 89 check_finite=check_finite)
90 return c
91
/opt/anaconda3/lib/python3.7/site-packages/scipy/linalg/decomp_cholesky.py in _cholesky(a, lower, overwrite_a, clean, check_finite)
15 """Common code for cholesky() and cho_factor()."""
16
---> 17 a1 = asarray_chkfinite(a) if check_finite else asarray(a)
18 a1 = atleast_2d(a1)
19
/opt/anaconda3/lib/python3.7/site-packages/numpy/lib/function_base.py in asarray_chkfinite(a, dtype, order)
484 if a.dtype.char in typecodes['AllFloat'] and not np.isfinite(a).all():
485 raise ValueError(
--> 486 "array must not contain infs or NaNs")
487 return a
488
ValueError: array must not contain infs or NaNs
This usually happens on the first iteration of the cross check loop. When excluding the first simulation it sometimes get's a bit farther but it always get stuck at some point. This is true for both the binned and non-binned case.
When using the new new cross check analysis tool with a mean model:
This leads to the following error:
This usually happens on the first iteration of the cross check loop. When excluding the first simulation it sometimes get's a bit farther but it always get stuck at some point. This is true for both the binned and non-binned case.