# I'm having trouble finding where the shape error is

**URL:** <https://discourse.pymc.io/t/im-having-trouble-finding-where-the-shape-error-is/9672>\
**Category:** v5\
**Created:** [June 17, 2022, 2:20pm UTC](https://discourse.pymc.io/t/im-having-trouble-finding-where-the-shape-error-is/9672 "2022-06-17T14:20:23Z")\
**Posts on this page:** 4\
**Page:** 1

<div class="post-metadata">

**Author:** ![jordan.howell2](https://yyz2.discourse-cdn.com/flex036/user_avatar/discourse.pymc.io/jordan.howell2/32/1742_2.png) [@jordan.howell2](https://discourse.pymc.io/u/jordan.howell2)\
**Post date:** [June 17, 2022, 2:20pm UTC](https://discourse.pymc.io/t/im-having-trouble-finding-where-the-shape-error-is/9672/1 "2022-06-17T14:20:23Z")

</div>

Hello,

I have monthly, sales data for 16 different locations. I built a ‘prophet-like’ model based on @MBrouns video here ( [Hierarchical Time Series With Prophet and PyMC3 (Matthijs Brouns) - YouTube](https://www.youtube.com/watch?v=appLxcMLT9Y)

That stated, the model runs fine until I try to make it a hierarchical model with locations. The model samples then errors when it’s transforming the variables with a shape error. Below is the model code and error.

```auto
location_idx, locations = pd.factorize(df_sat['location'])
coords_ = {'locations':locations,
          'obs_id':np.arange(len(location_idx))}

with pm.Model(coords = coords_) as model:
    location_idx_ = pm.ConstantData('location_idx', location_idx, dims = 'obs_id')
    
    mu_k = pm.Normal('mu_k', 0, 100)
    sigma_k = pm.HalfCauchy('sigma_k', 5)
    k = pm.Normal('k', mu = mu_k, sigma = sigma_k, dims = 'locations')
    
    mu_m = pm.Normal('mu_m,', 0, 100)
    sigma_m = pm.HalfCauchy('sigma_m', 5)
    m = pm.Normal('m', mu=mu_m, sigma = sigma_m, dims = 'locations')
    
    delta = pm.Laplace('delta', 0, 0.1, shape=n_changepoints)
    
    growth = pm.Deterministic('growth', (k[location_idx_] + at.dot(a, delta)), dims = 'locations')
    offset = pm.Deterministic('offset', (m[location_idx_] + at.dot(a, -s * delta)), dims = 'locations')
    trend = pm.Deterministic('trend', growth[location_idx_] * t + offset[location_idx_], dims = 'locations')
    
    # yearly_mu = pm.Normal('yearly_mu', 0, 1)
    # yearly_sigma = pm.HalfCauchy('yearly_sigma', 0, 1)
    yearly_beta = pm.Normal('yearly_beta', mu = 0, sigma = 1, shape = n_components*2)
    yearly_seasonality = pm.Deterministic('yearly_seasonality',at.dot(yearly_X(t, 365.25/len(t)), yearly_beta))
    
    # monthly_mu = pm.Normal('monthly_mu', 0, 1)
    # monthly_sigma = pm.HalfCauchy('monthly_sigma', 0, 1)
    monthly_beta = pm.Normal('monthly_beta', mu = 0, sigma=5, shape = monthly_n_components*2)
    monthly_seasonality = pm.Deterministic('monthly_seasonality',at.dot(monthly_X(t, 30.5/len(t)), monthly_beta))
    
    predictions = pm.Deterministic('predictions', np.exp(trend[location_idx_] + yearly_seasonality + monthly_seasonality))
    
    pm.Poisson('obs',
              mu = predictions,
              observed=df_sat['eaches'],
              dims = 'obs_id'
        )

```

Error

```auto
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
/tmp/ipykernel_5309/3004907719.py in <module>
     34 )
     35 
---> 36 trace_locations = pymc.sampling_jax.sample_numpyro_nuts(tune=2000, draws = 2000)

/opt/conda/lib/python3.7/site-packages/pymc/sampling_jax.py in sample_numpyro_nuts(draws, tune, chains, target_accept, random_seed, initvals, model, var_names, progress_bar, keep_untransformed, chain_method, postprocessing_backend, idata_kwargs, nuts_kwargs)
    568 dims=dims,
    569 attrs=make_attrs(attrs, library=numpyro),
--> 570 **idata_kwargs,
    571 )
    572 

/opt/conda/lib/python3.7/site-packages/arviz/data/io_dict.py in from_dict(posterior, posterior_predictive, predictions, sample_stats, log_likelihood, prior, prior_predictive, sample_stats_prior, observed_data, constant_data, predictions_constant_data, warmup_posterior, warmup_posterior_predictive, warmup_predictions, warmup_log_likelihood, warmup_sample_stats, save_warmup, index_origin, coords, dims, pred_dims, pred_coords, attrs, **kwargs)
    457 pred_coords=pred_coords,
    458 attrs=attrs,
--> 459 **kwargs,
    460 ).to_inference_data()

/opt/conda/lib/python3.7/site-packages/arviz/data/io_dict.py in to_inference_data(self)
    333 return InferenceData(
    334 **{
--> 335 "posterior": self.posterior_to_xarray(),
    336 "sample_stats": self.sample_stats_to_xarray(),
    337 "log_likelihood": self.log_likelihood_to_xarray(),

/opt/conda/lib/python3.7/site-packages/arviz/data/base.py in wrapped(cls)
     63 if all((getattr(cls, prop_i) is None for prop_i in prop)):
     64 return None
---> 65 return func(cls)
     66 
     67 return wrapped

/opt/conda/lib/python3.7/site-packages/arviz/data/io_dict.py in posterior_to_xarray(self)
    102 dims=self.dims,
    103 attrs=posterior_attrs,
--> 104 index_origin=self.index_origin,
    105 ),
    106 dict_to_dataset(

/opt/conda/lib/python3.7/site-packages/arviz/data/base.py in dict_to_dataset(data, attrs, library, coords, dims, default_dims, index_origin, skip_event_dims)
    312 default_dims=default_dims,
    313 index_origin=index_origin,
--> 314 skip_event_dims=skip_event_dims,
    315 )
    316 return xr.Dataset(data_vars=data_vars, attrs=make_attrs(attrs=attrs, library=library))

/opt/conda/lib/python3.7/site-packages/arviz/data/base.py in numpy_to_data_array(ary, var_name, coords, dims, default_dims, index_origin, skip_event_dims)
    252 # filter coords based on the dims
    253 coords = {key: xr.IndexVariable((key,), data=np.asarray(coords[key])) for key in dims}
--> 254 return xr.DataArray(ary, coords=coords, dims=dims)
    255 
    256 

/opt/conda/lib/python3.7/site-packages/xarray/core/dataarray.py in __init__ (self, data, coords, dims, name, attrs, indexes, fastpath)
    404 data = _check_data_shape(data, coords, dims)
    405 data = as_compatible_data(data)
--> 406 coords, dims = _infer_coords_and_dims(data.shape, coords, dims)
    407 variable = Variable(dims, data, attrs, fastpath=True)
    408 indexes = dict(

/opt/conda/lib/python3.7/site-packages/xarray/core/dataarray.py in _infer_coords_and_dims(shape, coords, dims)
    153 if s != sizes[d]:
    154 raise ValueError(
--> 155 f"conflicting sizes for dimension {d!r}: "
    156 f"length {sizes[d]} on the data but length {s} on "
    157 f"coordinate {k!r}"

ValueError: conflicting sizes for dimension 'locations': length 766 on the data but length 16 on coordinate 'locations'

```

I’ve tried taking the `dims=locations` out in various places. Maybe this has to do with the shape of the delta variable but when I try to make `shape = (16, n_changepoints)`, another error pops up. I’m not sure where the shape error is actually coming from.

---

<div class="post-metadata">

**Author:** ![michaelosthege](https://yyz2.discourse-cdn.com/flex036/user_avatar/discourse.pymc.io/michaelosthege/32/371_2.png) [@michaelosthege](https://discourse.pymc.io/u/michaelosthege)\
**Post date:** [June 17, 2022, 2:54pm UTC](https://discourse.pymc.io/t/im-having-trouble-finding-where-the-shape-error-is/9672/2 "2022-06-17T14:54:18Z")

</div>

Looks like you’re running with a `v4.0.0` version?

Try to plot a model graph: `pm.model_to_graphviz(model)`  
It also shows the shapes and dims and could get you either an error or a visual representation that could help.

You can also evaluate the variable shapes to check if they’re correct: `delta.shape.eval()` etc.

---

<div class="post-metadata">

**Author:** ![jordan.howell2](https://yyz2.discourse-cdn.com/flex036/user_avatar/discourse.pymc.io/jordan.howell2/32/1742_2.png) [@jordan.howell2](https://discourse.pymc.io/u/jordan.howell2)\
**Post date:** [June 17, 2022, 3:10pm UTC](https://discourse.pymc.io/t/im-having-trouble-finding-where-the-shape-error-is/9672/3 "2022-06-17T15:10:12Z")

</div>

> [@michaelosthege](#):
>
> .shape.eval()

Thank you!

It’s definitely the delta:

 ![image](https://canada1.discourse-cdn.com/flex036/uploads/pymc3/original/2X/c/c74c10e53b65d3d78b0ae1f91aa6fb67466dc75e.png)

So the shape of ‘8’ represents the changepoints along time. I’m guessing I need changepoints for each of the 16 locations.

I tried making the `delta` parameter’s shape a tuple of `(n_changepoints, len(locations)`. That throws an error of

```auto
 `---------------------------------------------------------------------------
ShapeError Traceback (most recent call last)
/tmp/ipykernel_5309/4187442434.py in <module>
     31 mu = predictions,
     32 observed=df_sat['eaches'],
---> 33 dims = 'obs_id'
     34 )
     35 

/opt/conda/lib/python3.7/site-packages/pymc/distributions/distribution.py in __new__ (cls, name, *args, **kwargs)
    592 raise ValueError("Transformations for discrete distributions")
    593 
--> 594 return super(). __new__ (cls, name, *args, **kwargs)
    595 
    596 

/opt/conda/lib/python3.7/site-packages/pymc/distributions/distribution.py in __new__ (cls, name, rng, dims, initval, observed, total_size, transform, *args, **kwargs)
    276 dims=dims,
    277 transform=transform,
--> 278 initval=initval,
    279 )
    280 

/opt/conda/lib/python3.7/site-packages/pymc/model.py in register_rv(self, rv_var, name, data, total_size, dims, transform, initval)
   1311 # `rv_var` is potentially changed by `make_obs_var`,
   1312 # for example into a new graph for imputation of missing data.
-> 1313 rv_var = self.make_obs_var(rv_var, data, dims, transform)
   1314 
   1315 return rv_var

/opt/conda/lib/python3.7/site-packages/pymc/model.py in make_obs_var(self, rv_var, data, dims, transform)
   1338 if data.ndim != rv_var.ndim:
   1339 raise ShapeError(
-> 1340 "Dimensionality of data and RV don't match.", actual=data.ndim, expected=rv_var.ndim
   1341 )
   1342 

ShapeError: Dimensionality of data and RV don't match. (actual 1 != expected 2)

```

In fact, when I explicitly call out a 'tuple(16, 8)` in the shape, I get the following error:

```auto
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
/tmp/ipykernel_5309/3034880190.py in <module>
     10 m = pm.Normal('m', mu=mu_m, sigma = sigma_m, dims = 'locations')
     11 
---> 12 mu_delta = pm.Laplace('mu_delta', 0, 1, shape= tuple(n_groups, n_changepoints))
     13 sigma_delta = pm.HalfCauchy('sigma_delta', .05)
     14 delta = pm.Deterministic('delta',mu_delta*sigma_delta, dims='locations')

TypeError: tuple expected at most 1 arguments, got 2

```

Well at least it’s narrowed down to the `where`, I’m just not sure on the `what`.

---

<div class="post-metadata">

**Author:** ![michaelosthege](https://yyz2.discourse-cdn.com/flex036/user_avatar/discourse.pymc.io/michaelosthege/32/371_2.png) [@michaelosthege](https://discourse.pymc.io/u/michaelosthege)\
**Post date:** [June 18, 2022, 9:09am UTC](https://discourse.pymc.io/t/im-having-trouble-finding-where-the-shape-error-is/9672/4 "2022-06-18T09:09:55Z")

</div>

That type error is just because `tuple` expects an Iterable. You can do any of the following:

- `(n_groups, n_changepoints)`
- `[n_groups, n_changepoints]`
- `tuple([n_groups, n_changepoints])`

The third is unnecessarily complex… The other two are matter of personal preference.