sdaza
February 1, 2018, 8:53pm
1
Hello,
After averaging three models (see example http://docs.pymc.io/notebooks/model_averaging.html ), I am generating predictions using this code:
ppc_w = pm.sample_ppc_w(traces, 1000, models,
weights=comp.weight.sort_index(ascending=True),
progressbar=False)
I would like to create counterfactual predictions based on specific values (data), but there is no data argument in the sample_ppc_w
function.
Any ideas or suggestions on how to do it??
If you are trying to generate prediction conditioned on some specific input/data, you should set your input as theano.shared
variable, and set the new value for prediction before you do pm.sample_ppc_w
or pm.sample_ppc
. There is an example here:
http://docs.pymc.io/notebooks/posterior_predictive.html#Prediction
1 Like
Yefee
March 16, 2018, 4:04am
4
Hi junpenglao,
Could you say it clearly? To me, the sample_ppc_w would require different data inputs, how could we build our shared variables to fit the prediction?
sample_ppc_w works similar to sample_ppc, the only difference is in sample_ppc_w you input multiple model and trace. So your workflow would be:
setting up theano shared variable
use said shared variable to build and sample multiple model
set the value of shared variable to the new test data
run sample_ppc_w
1 Like
Check here and here . The notebooks were split up a while back and broke the old links.
Thanks @cluhmann
It seems that the latter link has been changed to posterior_predictive.html