# TypeError when combining constant and Normal distribution in function within deterministic node

**URL:** <https://discourse.pymc.io/t/typeerror-when-combining-constant-and-normal-distribution-in-function-within-deterministic-node/9219>\
**Category:** v5\
**Tags:** bug\
**Created:** [April 16, 2022, 9:04am UTC](https://discourse.pymc.io/t/typeerror-when-combining-constant-and-normal-distribution-in-function-within-deterministic-node/9219 "2022-04-16T09:04:34Z")\
**Posts on this page:** 7\
**Page:** 1

<div class="post-metadata">

**Author:** ![jonititan](https://yyz2.discourse-cdn.com/flex036/user_avatar/discourse.pymc.io/jonititan/32/5057_2.png) [@jonititan](https://discourse.pymc.io/u/jonititan)\
**Post date:** [April 16, 2022, 9:04am UTC](https://discourse.pymc.io/t/typeerror-when-combining-constant-and-normal-distribution-in-function-within-deterministic-node/9219/1 "2022-04-16T09:04:34Z")

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[testexample.py](https://discourse.pymc.io/uploads/short-url/vMwMFyzbwbgyNA94QQW7jbvzXOu.py) (1.2 KB)

> /home/jonititan/miniconda3/envs/pymc-dev-py39/lib/python3.9/site-packages/pymc/aesaraf.py:1005: UserWarning: The parameter ‘updates’ of aesara.function() expects an OrderedDict, got \<class ‘dict’\>. Using a standard dictionary here results in non-deterministic behavior. You should use an OrderedDict if you are using Python 2.7 (collections.OrderedDict for older python), or use a list of (shared, update) pairs. Do not just convert your dictionary to this type before the call as the conversion will still be non-deterministic.  
> aesara\_function = aesara.function(  
> Multiprocess sampling (4 chains in 4 jobs)  
> CompoundStep  
> Metropolis: [Number of Engines]  
> NUTS: [Electric motor power]  
> pymc.parallel\_sampling.RemoteTraceback: ----------------------------------------------------------| 0.00% [0/24000 00:00\<00:00 Sampling 4 chains, 0 divergences]  
> “”"  
> Traceback (most recent call last):  
> File “/home/jonititan/miniconda3/envs/pymc-dev-py39/lib/python3.9/site-packages/aesara/compile/function/types.py”, line 964, in **call**  
> self.fn()  
> TypeError: expected type\_num 1 (NPY\_INT8) got 7

> During handling of the above exception, another exception occurred:

> Traceback (most recent call last):  
> File “/home/jonititan/miniconda3/envs/pymc-dev-py39/lib/python3.9/site-packages/pymc/parallel\_sampling.py”, line 125, in run  
> self.\_start\_loop()  
> File “/home/jonititan/miniconda3/envs/pymc-dev-py39/lib/python3.9/site-packages/pymc/parallel\_sampling.py”, line 178, in \_start\_loop  
> point, stats = self.\_compute\_point()  
> File “/home/jonititan/miniconda3/envs/pymc-dev-py39/lib/python3.9/site-packages/pymc/parallel\_sampling.py”, line 203, in \_compute\_point  
> point, stats = self.\_step\_method.step(self.\_point)  
> File “/home/jonititan/miniconda3/envs/pymc-dev-py39/lib/python3.9/site-packages/pymc/step\_methods/compound.py”, line 42, in step  
> point, state = method.step(point)  
> File “/home/jonititan/miniconda3/envs/pymc-dev-py39/lib/python3.9/site-packages/pymc/step\_methods/arraystep.py”, line 208, in step  
> step\_res = self.astep(q)  
> File “/home/jonititan/miniconda3/envs/pymc-dev-py39/lib/python3.9/site-packages/pymc/step\_methods/metropolis.py”, line 240, in astep  
> accept = self.delta\_logp(q, q0)  
> File “/home/jonititan/miniconda3/envs/pymc-dev-py39/lib/python3.9/site-packages/aesara/compile/function/types.py”, line 977, in **call**  
> raise\_with\_op(  
> File “/home/jonititan/miniconda3/envs/pymc-dev-py39/lib/python3.9/site-packages/aesara/link/utils.py”, line 538, in raise\_with\_op  
> raise exc\_value.with\_traceback(exc\_trace)  
> File “/home/jonititan/miniconda3/envs/pymc-dev-py39/lib/python3.9/site-packages/aesara/compile/function/types.py”, line 964, in **call**  
> self.fn()  
> TypeError: expected type\_num 1 (NPY\_INT8) got 7  
> Apply node that caused the error: Elemwise{Composite{Switch(EQ(i0, i1), i2, i3)}}(InplaceDimShuffle{}.0, TensorConstant{2}, TensorConstant{0}, TensorConstant{-inf})  
> Toposort index: 6  
> Inputs types: [TensorType(int8, ()), TensorType(int8, ()), TensorType(int8, ()), TensorType(float64, ())]  
> Inputs shapes: [(), (), (), ()]  
> Inputs strides: [(), (), (), ()]  
> Inputs values: [array(4), array(2, dtype=int8), array(0, dtype=int8), array(-inf)]  
> Outputs clients: [[MakeVector{dtype=‘float64’}(Elemwise{Composite{Switch(EQ(i0, i1), i2, i3)}}.0, Electric motor power\_logprob)]]

> HINT: Re-running with most Aesara optimizations disabled could provide a back-trace showing when this node was created. This can be done by setting the Aesara flag ‘optimizer=fast\_compile’. If that does not work, Aesara optimizations can be disabled with ‘optimizer=None’.  
> HINT: Use the Aesara flag `exception_verbosity=high` for a debug print-out and storage map footprint of this Apply node.  
> “”"

> The above exception was the direct cause of the following exception:

> TypeError: expected type\_num 1 (NPY\_INT8) got 7  
> Apply node that caused the error: Elemwise{Composite{Switch(EQ(i0, i1), i2, i3)}}(InplaceDimShuffle{}.0, TensorConstant{2}, TensorConstant{0}, TensorConstant{-inf})  
> Toposort index: 6  
> Inputs types: [TensorType(int8, ()), TensorType(int8, ()), TensorType(int8, ()), TensorType(float64, ())]  
> Inputs shapes: [(), (), (), ()]  
> Inputs strides: [(), (), (), ()]  
> Inputs values: [array(4), array(2, dtype=int8), array(0, dtype=int8), array(-inf)]  
> Outputs clients: [[MakeVector{dtype=‘float64’}(Elemwise{Composite{Switch(EQ(i0, i1), i2, i3)}}.0, Electric motor power\_logprob)]]

> HINT: Re-running with most Aesara optimizations disabled could provide a back-trace showing when this node was created. This can be done by setting the Aesara flag ‘optimizer=fast\_compile’. If that does not work, Aesara optimizations can be disabled with ‘optimizer=None’.  
> HINT: Use the Aesara flag `exception_verbosity=high` for a debug print-out and storage map footprint of this Apply node.

> The above exception was the direct cause of the following exception:

> Traceback (most recent call last):  
> File “/home/jonititan/Projects/Napkin/testexample.py”, line 38, in   
> samples = pm.sample(draws=nb\_samples, random\_seed=1000)  
> File “/home/jonititan/miniconda3/envs/pymc-dev-py39/lib/python3.9/site-packages/pymc/sampling.py”, line 542, in sample  
> mtrace = \_mp\_sample(\*\*sample\_args, \*\*parallel\_args)  
> File “/home/jonititan/miniconda3/envs/pymc-dev-py39/lib/python3.9/site-packages/pymc/sampling.py”, line 1469, in \_mp\_sample  
> for draw in sampler:  
> File “/home/jonititan/miniconda3/envs/pymc-dev-py39/lib/python3.9/site-packages/pymc/parallel\_sampling.py”, line 460, in **iter**  
> draw = ProcessAdapter.recv\_draw(self.\_active)  
> File “/home/jonititan/miniconda3/envs/pymc-dev-py39/lib/python3.9/site-packages/pymc/parallel\_sampling.py”, line 349, in recv\_draw  
> raise error from old\_error  
> RuntimeError: Chain 2 failed.

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<div class="post-metadata">

**Author:** ![cluhmann](https://yyz2.discourse-cdn.com/flex036/user_avatar/discourse.pymc.io/cluhmann/32/3083_2.png) [@cluhmann](https://discourse.pymc.io/u/cluhmann)\
**Post date:** [April 16, 2022, 4:34pm UTC](https://discourse.pymc.io/t/typeerror-when-combining-constant-and-normal-distribution-in-function-within-deterministic-node/9219/2 "2022-04-16T16:34:40Z")

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Welcome!

Can you provide a minimal example that yields this error?

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**Author:** ![ricardoV94](https://yyz2.discourse-cdn.com/flex036/user_avatar/discourse.pymc.io/ricardov94/32/5775_2.png) [@ricardoV94](https://discourse.pymc.io/u/ricardoV94)\
**Post date:** [April 16, 2022, 8:47pm UTC](https://discourse.pymc.io/t/typeerror-when-combining-constant-and-normal-distribution-in-function-within-deterministic-node/9219/3 "2022-04-16T20:47:28Z")

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The problem probably comes from the use of `pm.Constant`. There is no PyMC sampler that is specialized for it, and the generic samplers are likely to suggest other values which will have zero probability.

Is there any reason why you are using it? It’s mostly an internal distribution and users shouldn’t really need it outside of specialized cases like in Mixtures.

In your example you can simply use python numerical values as constants. If you want something that shows in graphviz you can use `pm.Data` instead.

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**Author:** ![ricardoV94](https://yyz2.discourse-cdn.com/flex036/user_avatar/discourse.pymc.io/ricardov94/32/5775_2.png) [@ricardoV94](https://discourse.pymc.io/u/ricardoV94)\
**Post date:** [April 16, 2022, 8:53pm UTC](https://discourse.pymc.io/t/typeerror-when-combining-constant-and-normal-distribution-in-function-within-deterministic-node/9219/4 "2022-04-16T20:53:00Z")

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Also Constant output dtype is whatever the constant passed to it is. In you case it would be `at.as_tensor_variable(2)` why gets converted to 8bit integer. However, Metropolis, (the generic sampler that gets assigned to this variable), does not respect this and sends a 32 or 64 bit proposal which is rejected.

You can specify `c=at.constant(2, dtype="int64")` to avoid this error but it is still inefficient to sample constants in the graph.

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<div class="post-metadata">

**Author:** ![jonititan](https://yyz2.discourse-cdn.com/flex036/user_avatar/discourse.pymc.io/jonititan/32/5057_2.png) [@jonititan](https://discourse.pymc.io/u/jonititan)\
**Post date:** [April 16, 2022, 9:41pm UTC](https://discourse.pymc.io/t/typeerror-when-combining-constant-and-normal-distribution-in-function-within-deterministic-node/9219/5 "2022-04-16T21:41:35Z")

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Thank you I suspected it might be something of that nature but lacked experience with PyMC.  
I will have a go with those two proposed alternatives and update.

I’ll try using pm.Data first but naively it doesn’t make sense to me that a single discrete value provided via data should be computationally more efficient than a single discrete value provided as a constant.

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**Author:** ![ricardoV94](https://yyz2.discourse-cdn.com/flex036/user_avatar/discourse.pymc.io/ricardov94/32/5775_2.png) [@ricardoV94](https://discourse.pymc.io/u/ricardoV94)\
**Post date:** [April 17, 2022, 5:52am UTC](https://discourse.pymc.io/t/typeerror-when-combining-constant-and-normal-distribution-in-function-within-deterministic-node/9219/6 "2022-04-17T05:52:49Z")

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You are being mislead by the name in `pm.Constant`. That is not a constant but a distribution that outputs always a constant value in prior predictive and that when sampled has logp 0 for the value and -inf everywhere else.

The problem with that is that PyMC tries to sample it, the same way it tries to sample a Normal or Categorical variable, but that’s very inefficient because only one proposed value can ever be accepted and PyMC samplers don’t know that.

It is not treated as a constant, in the same way that `pm.Data` or a pure Pyhton number would be, but as a random variable

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**Author:** ![jonititan](https://yyz2.discourse-cdn.com/flex036/user_avatar/discourse.pymc.io/jonititan/32/5057_2.png) [@jonititan](https://discourse.pymc.io/u/jonititan)\
**Post date:** [April 18, 2022, 11:27am UTC](https://discourse.pymc.io/t/typeerror-when-combining-constant-and-normal-distribution-in-function-within-deterministic-node/9219/7 "2022-04-18T11:27:24Z")

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Yes that solved my issue thank you.  
I am now using ConstantData  
[https://docs.pymc.io/en/latest/api/generated/pymc.ConstantData.html#pymc.ConstantData](https://docs.pymc.io/en/latest/api/generated/pymc.ConstantData.html#pymc.ConstantData)