Open supreetr opened 6 years ago
Hello,
I faced the same issue some time ago. As I could not find any built-in function that would do that, I figured out the following with the existing functions:
– estimate model m on the train data;
– run m.unprovision()
to be able to change the data linked to m;
– change the data linked to m using _change_data_fountain(df)
, where df is your new data;
– run m.setUp()
.
After that, you should be able to get what you want by running m.loglike()
or m.probability()
! It looks kind of unnecessarily complex, but it does work in my case. Hope this helps!
As you might notice from the source code here, I am also in the process of bringing Larch version 5 online, which provides explicit compatibility directly with fit
and predict
interface of sklearn. An example will be posted soon(-ish).
I am trying to use a previously estimated model to score new data. Is there an equivalent of sklearn's predict function in Larch? Any help is much appreciated.