TypeError: weight_predictions() got an unexpected keyword argument 'progressbar'

1

Hi any suggestions please??? Thank you

ppc_w = pm.sample_posterior_predictive_w(
[trace_0, trace_2, trace_1],
[model_0, model_1, model_2],
comp.weight.sort_index(ascending=True),
progressbar=True,
)
FutureWarning: The function sample_posterior_predictive_w has been removed in PyMC 4.3.0. Switch to arviz.stats.weight_predictions

ppc_w = az.stats.weight_predictions(
[trace_0, trace_2, trace_1],
[model_0, model_2, model_1],
comp.weight.sort_index(ascending=True),
progressbar=True
)
TypeError: weight_predictions() got an unexpected keyword argument ‘progressbar’

2

It looks like arviz.stats.weight_predictions doesn’t take a progressbar argument. Did you check the docstring for this function? That’s always a good place to start.

3 
#Posteriors    

with model_0:
   pp_model0 = pm.sample_posterior_predictive(trace_0)
   
# Save the idata to a NetCDF file
# Open it here: https://filext.com/online-file-viewer.html
#  For the moment all chains and draws are empty, so I guess something is not working in the computations...
   
   idata = pm.sample(return_inferencedata=True) 
   
   print (idata)
   
   idata.to_netcdf("C:/Users/Ossama/Model_Muse/filename.nc")
   
   
   
#pp_model0= pm.sample_posterior_predictive(model_0)

print(pp_model0)

with model_1:
    pp_model1= pm.sample_posterior_predictive(trace_1)

with model_2:
    pp_model2= pm.sample_posterior_predictive(trace_2)
    
model0= az.from_dict({
    'observed_data':{'x': observed_data},
    'posterior_predictive': pp_model0
})
    
model1= az.from_dict({
    'observed_data':{'x': observed_data},
    'posterior_predictive': pp_model1
})
    
model2= az.from_dict({
    'observed_data':{'x': observed_data},
    'posterior_predictive': pp_model2
})
    
weights= [0.3, 0.7, 0.3]

ppc_w= az.stats.weight_predictions([model0, model1, model2], weights)
    
print(ppc_w)

I used this now but it shows me another error:

ValueError                                Traceback (most recent call last)
Cell In[4], line 45
     38 model2= az.from_dict({
     39     'observed_data':{'x': observed_data},
     40     'posterior_predictive': pp_model2
     41 })
     43 weights= [0.3, 0.7, 0.3]
---> 45 ppc_w= az.stats.weight_predictions([model0, model1, model2], weights)
     47 print(ppc_w)
     49 #ppc_2 = az.stats.weight_predictions(model2)
     50     
     51 #print(ppc_2)

File ~\anaconda3\Lib\site-packages\arviz\stats\stats.py:2078, in weight_predictions(idatas, weights)
   2075     raise ValueError("You should provide a list with at least two InferenceData objects")
   2077 if not all("posterior_predictive" in idata.groups() for idata in idatas):
-> 2078     raise ValueError(
   2079         "All the InferenceData objects must contain the `posterior_predictive` group"
   2080     )
   2082 if not all(idatas[0].observed_data.equals(idata.observed_data) for idata in idatas[1:]):
   2083     raise ValueError("The observed data should be the same for all InferenceData objects")

ValueError: All the InferenceData objects must contain the `posterior_predictive` group
4

I don’t think you need to do all those az.from_dict calls. pm.sample_posterior_predictive will return an InferenceData object with a posterior_predictive group.