GuassianRandomWalk fails for 2D shape

[Rish001]

Description of your problem

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

with pymc3.Model() as pmodel1:
    grw1 = pymc3.GaussianRandomWalk('grw1', mu=numpy.arange(4), shape=(3,4,))

grw1.random(size = None).shape

When shape is introduced in the form of (3,4,), I am getting an error message
ValueError: Input dimension mis-match. (input[0].shape[1] = 4, input[1].shape[1] = 3) for the line

grw1 = pymc3.GaussianRandomWalk('grw1', mu=numpy.arange(4), shape=(3,4,))

Please provide the full traceback.

ValueError                                Traceback (most recent call last)
<ipython-input-24-177b01fab8f8> in <module>
      1 with pymc3.Model() as pmodel1:
----> 2     grw1 = pymc3.GaussianRandomWalk('grw1', mu=pymc3.Normal('mu',numpy.arange(4),1e-4,shape = 4), shape=(3,4,))
      3 
      4 grw1.random(size = None).shape

/mnt/c/Users/RISHAV/pymc3/pymc3/distributions/distribution.py in __new__(cls, name, *args, **kwargs)
     81         else:
     82             dist = cls.dist(*args, **kwargs)
---> 83         return model.Var(name, dist, data, total_size, dims=dims)
     84 
     85     def __getnewargs__(self):

/mnt/c/Users/RISHAV/pymc3/pymc3/model.py in Var(self, name, dist, data, total_size, dims)
   1070                 with self:
   1071                     var = FreeRV(
-> 1072                         name=name, distribution=dist, total_size=total_size, model=self
   1073                     )
   1074                 self.free_RVs.append(var)

/mnt/c/Users/RISHAV/pymc3/pymc3/model.py in __init__(self, type, owner, index, name, distribution, total_size, model)
   1591                 np.ones(distribution.shape, distribution.dtype) * distribution.default()
   1592             )
-> 1593             self.logp_elemwiset = distribution.logp(self)
   1594             # The logp might need scaling in minibatches.
   1595             # This is done in `Factor`.

/mnt/c/Users/RISHAV/pymc3/pymc3/distributions/timeseries.py in logp(self, x)
    272             x_i = x[1:]
    273             mu, sigma = self._mu_and_sigma(self.mu, self.sigma)
--> 274             innov_like = Normal.dist(mu=x_im1 + mu, sigma=sigma).logp(x_i)
    275             return self.init.logp(x[0]) + tt.sum(innov_like)
    276         return self.init.logp(x)

/mnt/c/Users/RISHAV/pymc3/myvenv/lib/python3.6/site-packages/theano/tensor/var.py in __add__(self, other)
    126     def __add__(self, other):
    127         try:
--> 128             return theano.tensor.basic.add(self, other)
    129         # We should catch the minimum number of exception here.
    130         # Otherwise this will convert error when Theano flags

/mnt/c/Users/RISHAV/pymc3/myvenv/lib/python3.6/site-packages/theano/gof/op.py in __call__(self, *inputs, **kwargs)
    672                 thunk.outputs = [storage_map[v] for v in node.outputs]
    673 
--> 674                 required = thunk()
    675                 assert not required  # We provided all inputs
    676 

/mnt/c/Users/RISHAV/pymc3/myvenv/lib/python3.6/site-packages/theano/gof/op.py in rval()
    860 
    861         def rval():
--> 862             thunk()
    863             for o in node.outputs:
    864                 compute_map[o][0] = True

/mnt/c/Users/RISHAV/pymc3/myvenv/lib/python3.6/site-packages/theano/gof/cc.py in __call__(self)
   1737                 print(self.error_storage, file=sys.stderr)
   1738                 raise
-> 1739             reraise(exc_type, exc_value, exc_trace)
   1740 
   1741 

/mnt/c/Users/RISHAV/pymc3/myvenv/lib/python3.6/site-packages/six.py in reraise(tp, value, tb)
    701             if value.__traceback__ is not tb:
    702                 raise value.with_traceback(tb)
--> 703             raise value
    704         finally:
    705             value = None

ValueError: Input dimension mis-match. (input[0].shape[1] = 4, input[1].shape[1] = 3)

Please provide any additional information below.

As discussed in #3985 , It looks like logp made some incorrect assumptions on the shape of mu and sigma

[Rish001]

@lucianopaz is there any update on this?

[twiecki]

@Rish001 Do you want to do a PR on this?

[Rish001]

Yeah I do but I have to understand what incorrect assumption is current logp method making.

[Sayam753]

I suspect the source of bug to be these lines -

https://github.com/pymc-devs/pymc3/blob/91993d880d3e40bb81f2a06402940fde20af555f/pymc3/distributions/timeseries.py#L223-L224

This helper method chops off an array from first dimension, because we want the noise from Gaussian steps to contribute to (n-1) timesteps. Coming to the logp method, a (3, 4) shaped tensor is passed in and asked to compute logp from Normal distribution with mean of shape 3.
And broadcasting fails for tensors (3, 4) and (3).

I see two possible solutions

• Broadcast mu and sigma beforehand to core distribution shape.
• Chop off the first array iff the number of dimensions in mu and sigma are equal to those in distribution shape. Let theano take care of broadcasting semantics for us.

This same issue popped out in #4388, during logp computations of MvGaussianRandomWalk with 2 dimensional inputs.

An ideal solution for me will be to have an abstract RandomWalk class that takes care of shape handling for us. Then we inherit this class, and only change init and noise distributions. With this, any PyMC3 distribution can be used for Random Walk. Thoughts on this?

@Rish001 Want to do a PR?

[Rish001]

Yeah sure. Let me see if what you said works Thanks and regards Rishav Dutta Phone: 8910550956 mail to: eladutta82@gmail.com বৃহস্, 31 ডিসে., 2020 14:32 তারিখে Sayam Kumar <notifications@github.com> লিখেছেন:

…

I suspect the source of bug to be these lines - https://github.com/pymc-devs/pymc3/blob/91993d880d3e40bb81f2a06402940fde20af555f/pymc3/distributions/timeseries.py#L223-L224 This helper method chops off an array from first dimension, because we want the noise from Gaussian steps to contribute to (n-1) timesteps. Coming to the logp method, a (3, 4) shaped tensor is passed in and asked to compute logp from Normal distribution with mean of shape 3. And broadcasting fails for tensors (3, 4) and (3). I see two possible solutions - Broadcast mu and sigma beforehand to core distribution shape. - Chop off the first array iff the number of dimensions in mu and sigma are equal to those in distribution shape. Let theano take care of broadcasting semantics for us. This same issue popped out in #4388 <#4388>, during logp computations of MvGaussianRandomWalk with 2 dimensional inputs. An ideal solution for me will be to have an abstract RandomWalk class that takes care of shape handling for us. Then we inherit this class, and only change init and noise distributions. With this, any PyMC3 distribution can be used for Random Walk. Thoughts on this? @Rish001 <https://github.com/Rish001> Want to do a PR? — You are receiving this because you were mentioned. Reply to this email directly, view it on GitHub <#4010 (comment)>, or unsubscribe <https://github.com/notifications/unsubscribe-auth/AFBQMCWJQ5TVJ3ZJ2JC4ZZLSXQ43FANCNFSM4OXZYOQQ> .

[ricardoV94]

Tracking #5298

[ricardoV94]

Closed via #5298