Error for default params of NoneType in pm.distributions.distribution._draw_value()

[gilliss]

I'm raising this issue following discussion at
https://discourse.pymc.io/t/error-for-default-params-of-nonetype-in-pm-distributions-distribution-draw-value/2183

I came across the following error when using pm.distributions.continuous.TruncatedNormal.dist().random().

ValueError: Unexpected type in draw_value: <class 'NoneType'>

The error seems to be caused by the default values of None for the upper and lower parameters of the BoundedContinuous distribution object. That is,

>>> dist = pm.TruncatedNormal.dist()
>>> type(dist.mu), dist.mu
(<class 'theano.tensor.var.TensorConstant'>, TensorConstant{0})
>>> type(dist.sd), dist.sd
(<class 'theano.tensor.var.TensorConstant'>, TensorConstant{1.0})
>>> type(dist.upper), dist.upper
(<class 'NoneType'>, None)
>>> type(dist.lower), dist.lower
(<class 'NoneType'>, None)
>>> dist.random()
ValueError: ...

If I give a value to just one of 'upper' or 'lower', the error persists. If I give values to both upper and lower, the error is avoided.

>>> dist.lower, dist.upper = 0, np.inf
>>> dist.random()
array(1.15514827)

Am I using the random() method in an unintended way? Perhaps it's just required that the user supply upper and lower bounds to the respective kwargs in TruncatedNormal-- in that case, it may be desirable to make those into positional arguments (matching scipy.stats.truncnorm).

Please provide a minimal, self-contained, and reproducible example.

import pymc3 as pm
pm.TruncatedNormal.dist(lower = 0., upper = 100.).random() # no error
try: pm.TruncatedNormal.dist(lower = 0.).random() # error
except ValueError: print('ValueError')
try: pm.TruncatedNormal.dist(upper = 100.).random() # error
except ValueError: print('ValueError')
try: pm.TruncatedNormal.dist().random() # error
except ValueError: print('ValueError')

Please provide the full traceback.

Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
  File "/Applications/anaconda3/envs/Fit2/lib/python3.6/site-packages/pymc3/distributions/continuous.py", line 578, in random
    [self.mu, self.sd, self.lower, self.upper], point=point, size=size)
  File "/Applications/anaconda3/envs/Fit2/lib/python3.6/site-packages/pymc3/distributions/distribution.py", line 321, in draw_values
    evaluated[param_idx] = _draw_value(param, point=point, givens=givens.values(), size=size)
  File "/Applications/anaconda3/envs/Fit2/lib/python3.6/site-packages/pymc3/distributions/distribution.py", line 418, in _draw_value
    raise ValueError('Unexpected type in draw_value: %s' % type(param))
ValueError: Unexpected type in draw_value: <class 'NoneType'>

Please provide any additional information below.

Versions and main components

• PyMC3 Version: 3.5
• Theano Version: 1.0.2
• Python Version: 3.6.6
• Operating system: macOS 10.13.2
• How did you install PyMC3: conda

[junpenglao]

Maybe we can change:
https://github.com/pymc-devs/pymc3/blob/a80440935d96554bf15c92996c3bf6ffb6a43f62/pymc3/distributions/continuous.py#L578-L579
To something like self.lower = tt.as_tensor_variable(lower) if lower is not None else np.inf

[KrUciFieR-Jr]

I would like to work on this issue

[twiecki]

@KrUciFieR-Jr There's a PR that's almost done but seems to have gone stale: #3250 I think it's just missing resolving the last item using a try-except block. You could clone @gilliss's branch, add the commit, and do a separate PR here.

[Harivallabha]

Hi! This issue does seem to have gone stale. I'd like to take it up if no one is currently working on it :) @twiecki

[lucianopaz]

@Harivallabha, sorry, this issue should have been closed after merging #3250.

[lucianopaz]

Oops, actually I got confused. This issue is still open. @Harivallabha, if you're up for the challenge feel free to go ahead.

[Harivallabha]

Yep, thank you!

[gilliss]

Does pull request #3250 not address issue #3248? That's my fault if it doesn't.

[lucianopaz]

@gilliss, sorry, yes. I doubted myself wrongly before. This issue was closed by #3250

[Harivallabha]

Yeah, I just went through #3250 sometime ago, and was wondering about the same xD Seemed to have been resolved :)