Open omarcr opened 4 years ago
@joelrich started an issue (#317) like that but it seemingly received no feedback. I would also vote for a parallel implementation.
How would we implement it to run in parallel? joblib.Parallel
?
The new implementation of permutation importance in scikit-learn (not yet released) offers some parallelism: https://scikit-learn.org/dev/modules/generated/sklearn.inspection.permutation_importance.html https://scikit-learn.org/dev/modules/generated/sklearn.inspection.permutation_importance.html#sklearn.inspection.permutation_importance
I think @jnothman reference is the best that we currently have. Does anyone know if this will be ported to Eli? thanks,
It seems even for relatively small training sets, model (e.g. DecisionTreeClassifier, RandomForestClassifier) training is fast, but using permutation_importance on the trained models is incredibly slow. (Currently using model.featureimportances as alternative)
Is there a way to make:
perm = PermutationImportance(estimator, cv='prefit', n_iter=1).fit(X_window_test, Y_test)
fast?currently I am running an experiment with 3,179 features and the algorithm is too slow (even with cv=prefit) is there a way to make it faster?