ValueError:输入包含nan值-来自lmfit模型的输入，尽管输入不包含NaN [英] ValueError: The input contains nan values - from lmfit model despite the input not containing NaNs
问题描述
我正在尝试使用lmfit (链接 ，并且似乎无法找出为什么我在尝试拟合模型时总是得到ValueError: The input contains nan values的原因.
I'm trying to build a model using lmfit (link to docs) and I can't seems to find out why I keep getting a ValueError: The input contains nan values when I try to fit the model.
from lmfit import minimize, Minimizer, Parameters, Parameter, report_fit, Model
import numpy as np
def cde(t, Qi, at, vw, R, rhob_cb, al, d, r):
# t (time), is the independent variable
return Qi / (8 * np.pi * ((at * vw)/R) * t * rhob_cb * (np.sqrt(np.pi * ((al * vw)/R * t)))) * \
np.exp(- (R * (d - (t * vw)/ R)**2) / (4 * (al * vw) * t) - (R * r**2)/ (4 * (at * vw) * t))
model_cde = Model(cde)
# Allowed to vary
model_cde.set_param_hint('vw', value =10**-4, min=0.000001)
model_cde.set_param_hint('d', value = -0.038, min = 0.0001)
model_cde.set_param_hint('r', value = 5.637e-10)
model_cde.set_param_hint('at', value =0.1)
model_cde.set_param_hint('al', value =0.15)
# Fixed
model_cde.set_param_hint('Qi', value = 1000, vary = False)
model_cde.set_param_hint('R', value =1.7, vary = False)
model_cde.set_param_hint('rhob_cb', value =3000, vary = False)
# test data
data = [ 1.37, 1.51, 1.65, 1.79, 1.91, 2.02, 2.12, 2.2 ,
2.27, 2.32, 2.36, 2.38, 2.4 , 2.41, 2.42, 2.41, 2.4 ,
2.39, 2.37, 2.35, 2.33, 2.31, 2.29, 2.26, 2.23, 2.2 ,
2.17, 2.14, 2.11, 2.08, 2.06, 2.02, 1.99, 1.97, 1.94,
1.91, 1.88, 1.85, 1.83, 1.8 , 1.78, 1.75, 1.72, 1.7 ,
1.68, 1.65, 1.63, 1.61, 1.58]
time = list(range(5,250,5))
model_cde.fit(data, t= time)
产生以下错误:
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
<ipython-input-16-785fcc6a994b> in <module>()
----> 1 model_cde.fit(data, t= time)
/home/bprodz/.virtualenvs/phd_dev/lib/python3.5/site-packages/lmfit/model.py in fit(self, data, params, weights, method, iter_cb, scale_covar, verbose, fit_kws, **kwargs)
539 scale_covar=scale_covar, fcn_kws=kwargs,
540 **fit_kws)
--> 541 output.fit(data=data, weights=weights)
542 output.components = self.components
543 return output
/home/bprodz/.virtualenvs/phd_dev/lib/python3.5/site-packages/lmfit/model.py in fit(self, data, params, weights, method, **kwargs)
745 self.init_fit = self.model.eval(params=self.params, **self.userkws)
746
--> 747 _ret = self.minimize(method=self.method)
748
749 for attr in dir(_ret):
/home/bprodz/.virtualenvs/phd_dev/lib/python3.5/site-packages/lmfit/minimizer.py in minimize(self, method, params, **kws)
1240 val.lower().startswith(user_method)):
1241 kwargs['method'] = val
-> 1242 return function(**kwargs)
1243
1244
/home/bprodz/.virtualenvs/phd_dev/lib/python3.5/site-packages/lmfit/minimizer.py in leastsq(self, params, **kws)
1070 np.seterr(all='ignore')
1071
-> 1072 lsout = scipy_leastsq(self.__residual, vars, **lskws)
1073 _best, _cov, infodict, errmsg, ier = lsout
1074 result.aborted = self._abort
/home/bprodz/.virtualenvs/phd_dev/lib/python3.5/site-packages/scipy/optimize/minpack.py in leastsq(func, x0, args, Dfun, full_output, col_deriv, ftol, xtol, gtol, maxfev, epsfcn, factor, diag)
385 maxfev = 200*(n + 1)
386 retval = _minpack._lmdif(func, x0, args, full_output, ftol, xtol,
--> 387 gtol, maxfev, epsfcn, factor, diag)
388 else:
389 if col_deriv:
/home/bprodz/.virtualenvs/phd_dev/lib/python3.5/site-packages/lmfit/minimizer.py in __residual(self, fvars, apply_bounds_transformation)
369
370 out = self.userfcn(params, *self.userargs, **self.userkws)
--> 371 out = _nan_policy(out, nan_policy=self.nan_policy)
372
373 if callable(self.iter_cb):
/home/bprodz/.virtualenvs/phd_dev/lib/python3.5/site-packages/lmfit/minimizer.py in _nan_policy(a, nan_policy, handle_inf)
1430
1431 if contains_nan:
-> 1432 raise ValueError("The input contains nan values")
1433 return a
1434
ValueError: The input contains nan values
但是，以下检查NaN的结果证实我的数据中没有NaN值:
However the results of the following checks for NaNs confirms that there were no NaN values in my data:
print(np.any(np.isnan(data)), np.any(np.isnan(time)))
False False
到目前为止，我已经尝试将列表中的1和/或data和time都转换为numpy ndarrays，删除了第0步(如果存在被0除的错误)，显式指定了t是独立的，并且允许所有变量都可以变化.但是，这些都引发相同的错误.
So far I've tried converting 1 and/or both of data and time from lists to numpy ndarrays, removing the 0th time step (in case there was a dividing by 0 error), explicitly specifying the t as being independent and allowing all variables to vary. However these all throw the same error.
是否有人知道导致此错误的原因是什么?谢谢.
Does anyone have ideas what is causing this error to be thrown? Thanks.
推荐答案
我尝试使用scipy.optimize.curve_fit拟合模型并出现以下错误:
I tried to fit my model using scipy.optimize.curve_fit and got the following error:
/home/bprodz/.virtualenvs/phd_dev/lib/python3.4/site-packages/ipykernel/__main__.py:3: RuntimeWarning: invalid value encountered in sqrt
app.launch_new_instance()
这表明问题在于我的模型为np.sqrt()生成了一些负数.给定负数时，np.sqrt()的默认行为是输出nan 来源
Which suggests the problem is with my model generating some negative numbers for np.sqrt(). The default behaviour for np.sqrt() when given a negative number is to output nan as per this question. NB the np.sqrt can be set to raise an error if given a negative number be setting the following: np.seterr(all='raise') source
TIP I also asked for help in the lmfit google group and received the following helpful advice:
- 考虑将较长的公式分解成较小的部分，以使故障排除更加容易
- 使用
Model.eval()测试通过模型函数运行时将产生哪些特定参数
在这些(数字)情况下， -
np.ndarray通常优于python列表
- Consider breaking long formulas into smaller pieces to make troubleshooting easier
- Use
Model.eval()to test what certain parameters will produce when run through your model function np.ndarrayis generally superior to python lists in these (numerical) situations
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