Bad Initial Energy when observed var contains nan

Questions
1

I’m trying to predict using the model below:

with pm.Model() as model:
        smoothing_param = shared(smoothing)
        mu = pm.HalfNormal("mu", sigma=1e3)
        tau = pm.Exponential("tau", 1.0/1e3)
        z = pm.GaussianRandomWalk("z",
                               mu=mu,
                               tau=tau / (1.0 - smoothing_param),
                               shape=self.y_len)
        y = pm.Normal("y",
                        mu=z,
                        tau=tau / smoothing_param,
                        observed=self.y_shared)

The model samples fine when the y_shared values are:

array([0.3373, 0.3919, 0.3656, 0.427 , 0.5428, 0.58  , 0.636 , 0.6937,
       0.7287, 0.7345, 0.7517, 0.7874, 0.8003, 0.8062, 0.81  , 0.8394,
       0.8432, 0.8794])

but if I pad the observation with NaNs to predict, I get the following error:

Auto-assigning NUTS sampler...
Initializing NUTS using jitter+adapt_diag...
Multiprocess sampling (4 chains in 4 jobs)
NUTS: [sigma, intercept, step_m, glb_m]
Sampling 4 chains:   0%|          | 0/10000 [00:00<?, ?draws/s]/.../lib/python3.7/site-packages/numpy/core/fromnumeric.py:3257: RuntimeWarning: Mean of empty slice.
  out=out, **kwargs)
/.../lib/python3.7/site-packages/numpy/core/fromnumeric.py:3257: RuntimeWarning: Mean of empty slice.
  out=out, **kwargs)
/.../lib/python3.7/site-packages/numpy/core/fromnumeric.py:3257: RuntimeWarning: Mean of empty slice.
  out=out, **kwargs)
/...lib/python3.7/site-packages/numpy/core/fromnumeric.py:3257: RuntimeWarning: Mean of empty slice.
  out=out, **kwargs)
Sampling 4 chains:   0%|          | 0/10000 [00:00<?, ?draws/s]
Bad initial energy, check any log probabilities that are inf or -inf, nan or very small:
y   NaN

Having read through a number of posts and comments on predicting/interpolating, my understanding was that this is the way to sample from unseen/oos data.

What should I do differently here?

c.f. Getting 'Bad initial energy: inf' when trying to sample simple model

2

Likely the jitter is causing problem here, Try: pm.sample(init=‘adapt_diag’)

3

Hi @junpenglao, thanks for the quick response. I’ve tried changing the initiation of the sampler, but it doesn’t resolve the issue.

here is the observed var I’m testing on:

array([0.4676, 0.5269, nan, nan, nan, 0.63 , 0.66 , 0.6945,
0.7167, 0.7425, 0.76 , 0.7949, 0.79 , 0.8345, 0.84 , 0.8456,
0.8824, 0.8654])

The MAP of the model is:

{‘mu_log__’: array(5.68196393),
‘tau_log__’: array(6.54124236),
‘z’: array([-7.37099586e-05, 1.62284820e-08, 0.00000000e+00, 0.00000000e+00,
0.00000000e+00, 1.94039545e-08, 2.03279524e-08, 2.13905499e-08,
2.20743083e-08, 2.28689464e-08, 2.34079451e-08, 2.44828626e-08,
2.43319430e-08, 2.57025398e-08, 2.58719394e-08, 2.60444190e-08,
2.71778563e-08, 7.37510149e-05]),
‘mu’: array(293.52532794),
‘tau’: array(693.14718246)}

and the test_point is:

mu_log__ -7.700000e-01
tau_log__ -1.060000e+00
z -1.500322e+10
y NaN
Name: Log-probability of test_point, dtype: float64

you’ll see in the MAP output that the missing / nan values in the observed var are returned as 0. This seems normal, and yet, still no go. Any ideas?

4

Oh, if your observation contains nan it’s not going to work. You can either used a masked array or remove the nans

5

hi, thanks for the reply. yea, the odd thing is that if I enter the observation as a Shared tensor it doesn’t work, but if I enter it as a pandas.Series it works, even with the nans. this was recommended to me by DanWeitzenfeld, at: Out-of-sample random-walk time series prediction

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