I have a model with a couple of predictor variables, stored in a dataframe. I’m doing some deterministic transformations of those variables, and for some reason, some transforms give me `ValueError: length not known: Elemwise{<operator>,no_inplace}`

and some don’t.

Here’s a sample, where `t0`

and `theta`

are RVs in the model and `t`

is a predictor variable coming from the data:

```
with pm.Model() as mod:
theta = pm.Lognormal("theta", 0.0, 3.0)
t0 = pm.Lognormal("t0", 0.0, 3.0)
timeexp = -(t - t0)/theta
```

This yields (after some long Traceback, in full below):

```
ValueError: length not known: Elemwise{sub,no_inplace} [id A] ''
|TensorConstant{[ -1. -..75. -176.]} [id B]
|InplaceDimShuffle{x} [id C] ''
|Elemwise{true_div,no_inplace} [id D] ''
|ViewOp [id E] 't0'
| |Elemwise{exp,no_inplace} [id F] ''
| |t0_log__ [id G]
|ViewOp [id H] 'theta'
|Elemwise{exp,no_inplace} [id I] ''
|theta_log__ [id J]
```

Weirdly, if in the model I specify (just as an experiment) `timeexp = t * t0/theta`

, I get a similar error, but if I use `timeexp = t0/theta * t`

, it initializes fine. I’m not sure what’s going on

I also tried defining it as `timeexp = pm.Deterministic('timeexp', -(t.to_list()-t0)/theta)`

- this avoids the error but then my output contains a whole list of `timeexp`

variables, `timeexp 0, timeexp 1... timeexp <len(t)>`

, which is not at all what I expect or intend - `timeexp`

is supposed to be a simple transformation of the predictor `t`

.

What’s going on / how can I get it to read the predictor in correctly?

Full traceback:

```
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
<ipython-input-65-25746c5d3158> in <module>
3 t0 = pm.Lognormal("t0", 0.0, 3.0)
4
----> 5 timeexp = -(t-t0)/theta
6
~\AppData\Roaming\Python\Python38\site-packages\pandas\core\ops\common.py in new_method(self, other)
63 other = item_from_zerodim(other)
64
---> 65 return method(self, other)
66
67 return new_method
~\AppData\Roaming\Python\Python38\site-packages\pandas\core\ops\__init__.py in wrapper(left, right)
343 result = arithmetic_op(lvalues, rvalues, op)
344
--> 345 return left._construct_result(result, name=res_name)
346
347 wrapper.__name__ = op_name
~\AppData\Roaming\Python\Python38\site-packages\pandas\core\series.py in _construct_result(self, result, name)
2755 # We do not pass dtype to ensure that the Series constructor
2756 # does inference in the case where `result` has object-dtype.
-> 2757 out = self._constructor(result, index=self.index)
2758 out = out.__finalize__(self)
2759
~\AppData\Roaming\Python\Python38\site-packages\pandas\core\series.py in __init__(self, data, index, dtype, name, copy, fastpath)
299 raise TypeError(f"'{type(data).__name__}' type is unordered")
300 else:
--> 301 data = com.maybe_iterable_to_list(data)
302
303 if index is None:
~\AppData\Roaming\Python\Python38\site-packages\pandas\core\common.py in maybe_iterable_to_list(obj)
277 """
278 if isinstance(obj, abc.Iterable) and not isinstance(obj, abc.Sized):
--> 279 return list(obj)
280 return obj
281
~\AppData\Roaming\Python\Python38\site-packages\theano\tensor\var.py in __iter__(self)
638 def __iter__(self):
639 try:
--> 640 for i in xrange(theano.tensor.basic.get_vector_length(self)):
641 yield self[i]
642 except TypeError:
~\AppData\Roaming\Python\Python38\site-packages\theano\tensor\basic.py in get_vector_length(v)
4826 else:
4827 msg = str(v)
-> 4828 raise ValueError("length not known: %s" % msg)
4829
4830
ValueError: length not known: Elemwise{sub,no_inplace} [id A] ''
|TensorConstant{[ 1. 2...175. 176.]} [id B]
|InplaceDimShuffle{x} [id C] ''
|ViewOp [id D] 't0'
|Elemwise{exp,no_inplace} [id E] ''
|t0_log__ [id F]
```