问题
This is my situation. I have over 400 features, many of which are probably useless and often zero. I would like to be able to:
- train an model with a subset of those features
- query that model for the features actually used to build that model
- build a H2OFrame containing just those features (I get a sparse list of non-zero values for each row I want to predict.)
- pass this newly constructed frame to H2OModel.predict() to get a prediction
I am pretty sure what found is unsupported but works for now (v 3.13.0.341). Is there a more robust/supported way of doing this?
model._model_json['output']['names']
The response variable appears to be the last item in this list.
In a similar vein, it would be nice to have a supported way of finding out which H2O version that the model was built under. I cannot find the version number in the json.
回答1:
If you want to know which feature columns the model used after you have built a model you can do the following in python:
my_training_frame = your_model.actual_params['training_frame']
which will return some frame id
and then you can do
col_used = h2o.get_frame(my_training_frame)
col_used
EDITED (after comment was posted)
to get the columns use:
enter code herecol_used.columns
a quick way to check the version of a saved binary model is to try and load it into h2o, if it loads it is the same version of h2o, if it isn't you will get a warning.
you can also open the saved model file, the first line will list the version of H2O used to create it.
for a model saved as a mojo you can look at the model.ini file, it will list the version of H2O
来源：https://stackoverflow.com/questions/45153176/is-there-a-supported-way-to-get-list-of-features-used-by-a-h2o-model-during-its