Pm.traceplot() error

I built a model using a costum likelihood, including both contiunous and discrete RVs, and ran sampling from posterior successfully, with the following codes:

with model:    
    step = pm.Metropolis()
    step.discrete = discrete_status
    step.any_discrete = True
    step.all_discrete = False
    samples = pm.sample(1000, step=step,cores=1,tune=500)

I inspected the results using pm.summary(), and it looks normal.

However, when I type the following codes for plotting parameters’ posterior distribution:


it returned error message:

AttributeError: 'MultiObservedRV' object has no attribute 'observations'

Please help! Many thanks

1 Like

Hi, this is probably a bug in ArviZ lib. Can you create an issue in github so we remember to fix it.

Ps. If needed, we can create the issue for you

1 Like

Thank you for response. Sure, please feel free to create the issue. Let me know if you need further information from me.

1 Like

A reproducible example would be useful – I don’t know an easy way of getting a MultiObservedRV off the top of my head! I guess you are feeding observations into more than one spot?

Please see my model definition:

import pandas as pd #To work with dataset
import numpy as np #Math library
import seaborn as sns #Graph library that use matplot in background
import matplotlib.pyplot as plt #to plot some parameters
import pymc3 as pm

import theano.tensor as tt

#load data
X = pd.read_csv('/my/data/file/path')
X_values = X.to_numpy()

def my_density(theta,W):
    def logp(X):
        #X is the data containing the first 23 features
        def log_expo(lam,x):
            return( tt.sum(tt.log(lam) - lam * x) )

        def log_bernoulli(p,x):
            return( tt.sum(1.* ( tt.switch( x, tt.log(p), tt.log(1 - p) ))) )
        LL = np.array([
        return(,LL) )

#hyperparameters for exponential distribution
hyper_expo_lower = 0.000001
hyper_expo_upper = 10
#hyperparameters for prior of bernoulli distribution : X3 - X23
hyper_bern_lower = 0
hyper_bern_upper = 1

with pm.Model() as model:
    ##set prior for parameters
    #for X0 
    r_0 =  pm.Uniform('r_0',lower = hyper_expo_lower, upper = hyper_expo_upper)
    #for X1
    p_1 =  pm.Uniform('p_1',lower = hyper_bern_lower, upper = hyper_bern_upper)

    tetha = np.array([
              r_0, #for X0
              p_1, #for X1

##set likelihood
joint_obs = pm.DensityDist('joint_obs', 
                           observed={'X' : X, #samples for X0 - X1

In the above model, This model contains 2 random variables (X has 2 dimensions). X is the data matrix: each column is a random variable, and each row is a sample.

my_density() is the costum likelihood. It accepts 2 arguments. The first argument tetha contains parameters for the two dimensions.

The second argument W is a weight vector used in likelihood definition, whose values are known.

The data is input as: ‘X’ : X

Let me know if you find any problem.

I think any DensityDist would give this error - it is a tricky one because you can feed non-observed to DensityDist.

Is there any way to solve the problem?
Can you tell me how can I get these samples (dataframe each column is a parameter and each row is a sample from the joint distribution of parameters)? If I can get these samples, I can draw them using other python visualization tools.

You can use pm.trace_to_dataframe for that.

I made a PR in ArviZ. What is still missing, is minimal reproducible what we could test against.

That said, there are some other corner cases for DensityDist that will probably fail in future.