# Hierarchical Model broadcast error

**URL:** <https://discourse.pymc.io/t/hierarchical-model-broadcast-error/6812>\
**Category:** Questions\
**Created:** [February 22, 2021, 10:02am UTC](https://discourse.pymc.io/t/hierarchical-model-broadcast-error/6812 "2021-02-22T10:02:02Z")\
**Posts on this page:** 2\
**Page:** 1

<div class="post-metadata">

**Author:** ![kdubovikov](https://yyz2.discourse-cdn.com/flex036/user_avatar/discourse.pymc.io/kdubovikov/32/3692_2.png) [@kdubovikov](https://discourse.pymc.io/u/kdubovikov)\
**Post date:** [February 22, 2021, 10:02am UTC](https://discourse.pymc.io/t/hierarchical-model-broadcast-error/6812/1 "2021-02-22T10:02:02Z")

</div>

I am trying to build a hierarchical model, where records are divided into two categories: score classes and topics. I have a dataset of 239417 with 4 score classes and 12 topics. The model I am trying to build is raising and exception:

```
import pymc3 as pm

topic_score_model_2level = pm.Model()
n_score_classes = 4
n_topics = len(topics) * n_score_classes

batch_size = 64

data = indexed_score_topics_df
# X = pm.Minibatch(data.score.astype('float32').values, batch_size=batch_size)
# idx_topic = pm.Minibatch(data.index_topic.values, batch_size=batch_size)
# idx_score_class = pm.Minibatch(data.index_score_class.values, batch_size=batch_size)

X = data.score.astype('float32').values
idx_topic = data.index_topic.values
idx_score_class = data.index_score_class.values

with topic_score_model_2level:
    global_mu = pm.Normal('global_mu', mu=10, sigma=25)
    global_sd = pm.Uniform('global_sd', lower=0, upper=50)
    
    score_class_mu = pm.Normal(
       'score_class_mu', 
       mu=global_mu, 
       sd=global_sd, 
       shape=n_score_classes
    )
    
    score_class_sd = pm.Uniform(
        'score_class_sd', 
        lower=100, 
        upper=200,
        shape=n_score_classes
    )
    
    topic = pm.Normal(
       'topic_mu', 
       mu=score_class_mu[idx_score_class], 
       sd=score_class_sd[idx_score_class], 
       shape=n_topics
    )
    
    topic_intercept = pm.Flat("topic_intercept")

    theta = np.exp(topic_intercept + topic[idx_topic])
    

    y = pm.Poisson('y', mu=theta, observed=X, total_size=len(data))

    advi_fit = pm.fit(n=10000, method='fullrank_advi')

```

The exception is the following:

```
ValueError Traceback (most recent call last)
<ipython-input-321-1510f670ffea> in <module>
     34 )
     35 
---> 36 topic = pm.Normal(
     37 'topic_mu',
     38 mu=score_class_mu[idx_score_class],

~/miniconda3/envs/ds38/lib/python3.8/site-packages/pymc3/distributions/distribution.py in __new__ (cls, name, *args, **kwargs)
    120 else:
    121 dist = cls.dist(*args, **kwargs)
--> 122 return model.Var(name, dist, data, total_size, dims=dims)
    123 
    124 def __getnewargs__ (self):

~/miniconda3/envs/ds38/lib/python3.8/site-packages/pymc3/model.py in Var(self, name, dist, data, total_size, dims)
   1135 if getattr(dist, "transform", None) is None:
   1136 with self:
-> 1137 var = FreeRV(name=name, distribution=dist, total_size=total_size, model=self)
   1138 self.free_RVs.append(var)
   1139 else:

~/miniconda3/envs/ds38/lib/python3.8/site-packages/pymc3/model.py in __init__ (self, type, owner, index, name, distribution, total_size, model)
   1666 self.distribution = distribution
   1667 self.tag.test_value = (
-> 1668 np.ones(distribution.shape, distribution.dtype) * distribution.default()
   1669 )
   1670 self.logp_elemwiset = distribution.logp(self)

ValueError: operands could not be broadcast together with shapes (48,) (239417,) 

```

How should change the indexing so that pymc3 won’t try to broadcast my data sample to number of topic distributions? 48 is the total number of topic distributions = 4 score classes (level 1) \* 12 topics in each (level 2). I thought that `total_size=len(data)` should correct this behaviour. `data.index_topic` and `data.index_score_class.values` are simple indices with category ids.

---

<div class="post-metadata">

**Author:** ![kdubovikov](https://yyz2.discourse-cdn.com/flex036/user_avatar/discourse.pymc.io/kdubovikov/32/3692_2.png) [@kdubovikov](https://discourse.pymc.io/u/kdubovikov)\
**Post date:** [February 23, 2021, 3:19pm UTC](https://discourse.pymc.io/t/hierarchical-model-broadcast-error/6812/2 "2021-02-23T15:19:05Z")

</div>

Problem solved: I was using a vector of all score\_class ids ( `idx_score_class`) as index for `score_class_mu` distribution. This didn’t make sense as `topic_mu` variable has shape of `n_topics`.  
I have changed the index to represent “which score class should I use for each topic?”, thus a vector of length `n_topics`, which consists of `score_class` identifiers. The model worked as expected afterwards.

This post may help anyone with the same kind of problem: [Deep in the Weeds: Complex Hierarchical Models in PyMC3 - Data Bozo](https://www.databozo.com/deep-in-the-weeds-complex-hierarchical-models-in-pymc3)