Why is my first attempt at a regression model giving me this error?

Misc.
1

Hello.

I tried to run a bayesian regression model based off the tutorial, only modified slightly for a y variable that’s a poisson mix.

I’m getting the following error:

Traceback (most recent call last):

  File "<ipython-input-25-4f2de26ca653>", line 3, in <module>
    loss = svi.step(x, y)

  File "C:\Users\JORDAN.HOWELL.GITDIR\AppData\Local\Continuum\anaconda3\envs\torch_env\lib\site-packages\pyro\infer\svi.py", line 128, in step
    loss = self.loss_and_grads(self.model, self.guide, *args, **kwargs)

  File "C:\Users\JORDAN.HOWELL.GITDIR\AppData\Local\Continuum\anaconda3\envs\torch_env\lib\site-packages\pyro\infer\trace_elbo.py", line 126, in loss_and_grads
    for model_trace, guide_trace in self._get_traces(model, guide, args, kwargs):

  File "C:\Users\JORDAN.HOWELL.GITDIR\AppData\Local\Continuum\anaconda3\envs\torch_env\lib\site-packages\pyro\infer\elbo.py", line 170, in _get_traces
    yield self._get_trace(model, guide, args, kwargs)

  File "C:\Users\JORDAN.HOWELL.GITDIR\AppData\Local\Continuum\anaconda3\envs\torch_env\lib\site-packages\pyro\infer\trace_elbo.py", line 53, in _get_trace
    "flat", self.max_plate_nesting, model, guide, args, kwargs)

  File "C:\Users\JORDAN.HOWELL.GITDIR\AppData\Local\Continuum\anaconda3\envs\torch_env\lib\site-packages\pyro\infer\enum.py", line 44, in get_importance_trace
    guide_trace = poutine.trace(guide, graph_type=graph_type).get_trace(*args, **kwargs)

  File "C:\Users\JORDAN.HOWELL.GITDIR\AppData\Local\Continuum\anaconda3\envs\torch_env\lib\site-packages\pyro\poutine\trace_messenger.py", line 187, in get_trace
    self(*args, **kwargs)

  File "C:\Users\JORDAN.HOWELL.GITDIR\AppData\Local\Continuum\anaconda3\envs\torch_env\lib\site-packages\pyro\poutine\trace_messenger.py", line 165, in __call__
    ret = self.fn(*args, **kwargs)

  File "C:\Users\JORDAN.HOWELL.GITDIR\AppData\Local\Continuum\anaconda3\envs\torch_env\lib\site-packages\pyro\nn\module.py", line 413, in __call__
    return super().__call__(*args, **kwargs)

  File "C:\Users\JORDAN.HOWELL.GITDIR\AppData\Local\Continuum\anaconda3\envs\torch_env\lib\site-packages\torch\nn\modules\module.py", line 722, in _call_impl
    result = self.forward(*input, **kwargs)

  File "C:\Users\JORDAN.HOWELL.GITDIR\AppData\Local\Continuum\anaconda3\envs\torch_env\lib\site-packages\pyro\infer\autoguide\guides.py", line 681, in forward
    self._setup_prototype(*args, **kwargs)

  File "C:\Users\JORDAN.HOWELL.GITDIR\AppData\Local\Continuum\anaconda3\envs\torch_env\lib\site-packages\pyro\infer\autoguide\guides.py", line 821, in _setup_prototype
    super()._setup_prototype(*args, **kwargs)

  File "C:\Users\JORDAN.HOWELL.GITDIR\AppData\Local\Continuum\anaconda3\envs\torch_env\lib\site-packages\pyro\infer\autoguide\guides.py", line 579, in _setup_prototype
    super()._setup_prototype(*args, **kwargs)

  File "C:\Users\JORDAN.HOWELL.GITDIR\AppData\Local\Continuum\anaconda3\envs\torch_env\lib\site-packages\pyro\infer\autoguide\guides.py", line 158, in _setup_prototype
    self.prototype_trace = poutine.block(poutine.trace(model).get_trace)(*args, **kwargs)

  File "C:\Users\JORDAN.HOWELL.GITDIR\AppData\Local\Continuum\anaconda3\envs\torch_env\lib\site-packages\pyro\poutine\messenger.py", line 12, in _context_wrap
    return fn(*args, **kwargs)

  File "C:\Users\JORDAN.HOWELL.GITDIR\AppData\Local\Continuum\anaconda3\envs\torch_env\lib\site-packages\pyro\poutine\trace_messenger.py", line 187, in get_trace
    self(*args, **kwargs)

  File "C:\Users\JORDAN.HOWELL.GITDIR\AppData\Local\Continuum\anaconda3\envs\torch_env\lib\site-packages\pyro\poutine\trace_messenger.py", line 165, in __call__
    ret = self.fn(*args, **kwargs)

  File "C:\Users\JORDAN.HOWELL.GITDIR\AppData\Local\Continuum\anaconda3\envs\torch_env\lib\site-packages\pyro\poutine\messenger.py", line 12, in _context_wrap
    return fn(*args, **kwargs)

  File "C:\Users\JORDAN.HOWELL.GITDIR\AppData\Local\Continuum\anaconda3\envs\torch_env\lib\site-packages\pyro\poutine\messenger.py", line 12, in _context_wrap
    return fn(*args, **kwargs)

  File "C:\Users\JORDAN.HOWELL.GITDIR\AppData\Local\Continuum\anaconda3\envs\torch_env\lib\site-packages\pyro\nn\module.py", line 413, in __call__
    return super().__call__(*args, **kwargs)

  File "C:\Users\JORDAN.HOWELL.GITDIR\AppData\Local\Continuum\anaconda3\envs\torch_env\lib\site-packages\torch\nn\modules\module.py", line 722, in _call_impl
    result = self.forward(*input, **kwargs)

  File "<ipython-input-8-1e297b618560>", line 10, in forward
    mean = self.linear(x).squeeze(-1)

  File "C:\Users\JORDAN.HOWELL.GITDIR\AppData\Local\Continuum\anaconda3\envs\torch_env\lib\site-packages\pyro\nn\module.py", line 413, in __call__
    return super().__call__(*args, **kwargs)

  File "C:\Users\JORDAN.HOWELL.GITDIR\AppData\Local\Continuum\anaconda3\envs\torch_env\lib\site-packages\torch\nn\modules\module.py", line 722, in _call_impl
    result = self.forward(*input, **kwargs)

  File "C:\Users\JORDAN.HOWELL.GITDIR\AppData\Local\Continuum\anaconda3\envs\torch_env\lib\site-packages\torch\nn\modules\linear.py", line 91, in forward
    return F.linear(input, self.weight, self.bias)

  File "C:\Users\JORDAN.HOWELL.GITDIR\AppData\Local\Continuum\anaconda3\envs\torch_env\lib\site-packages\torch\nn\functional.py", line 1672, in linear
    if input.dim() == 2 and bias is not None:

AttributeError: 'DesignMatrix' object has no attribute 'dim'

It’s a 15 feature regression model with a Poisson distributed target variable.

Here is the model code:

class BayesianRegression(PyroModule):
    def __init__(self, in_features, out_features):
        super().__init__()
        self.linear = PyroModule[nn.Linear](in_features, out_features)
        self.linear.weight = PyroSample(dist.Normal(0., 1.).expand([out_features, in_features]).to_event(2))
        self.linear.bias = PyroSample(dist.Normal(0., 10.).expand([out_features]).to_event(1))

    def forward(self, x, y=None):
        sigma = pyro.sample("sigma", dist.Uniform(0., 10.))
        mean = self.linear(x).squeeze(-1)
        with pyro.plate("data", x.shape[0]):
            rate = self.linear(x).squeeze(-1).exp()
            obs = pyro.sample("obs", dist.Poisson(rate), obs=y)
        return mean
    
model = BayesianRegression(15, 1)    
guide = AutoDiagonalNormal(model)    

adam = pyro.optim.Adam({"lr": 0.03})
svi = SVI(model, guide, adam, loss=Trace_ELBO())

num_iterations = 1500
pyro.clear_param_store()
for j in range(num_iterations):
    # calculate the loss and take a gradient step
    loss = svi.step(x, y)
    if j % 100 == 0:
        print("[iteration %04d] loss: %.4f" % (j + 1, loss / len(x)))

As I stated earlier, it’s a copy/paste of the tutorial changing obs to Poisson.

2

Disregard. I transformed the data from a matrix to a tensor and now it’s running!

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