Ensembling Models

Using the make_ensemble() function makes it easy to combine models.

By default, ensembling is based on the db_name for the nodes in each input graph. Nodes which have the same name will be assigned an ensemble node which combines the different versions of that quantity, and additionally calculates the mean and standard deviation.

It is easy to make an ensemble from a glob string or a list of directories where the models are saved:

from hippynn.graphs import make_ensemble
model_form = '../../collected_models/quad0_b512_p5_GPU*'
ensemble_graph, ensemble_info = make_ensemble(model_form)

The ensemble graph takes the inputs which are required for all of the models in the ensemble. The ensemble_info object provides the counts for the inputs and targets of the ensemble and the counts of those corresponding quantities across the ensemble members.

A typical use case would be to then build a Predictor or ASE Calculator from the ensemble. See /examples/ensembling_models.py for a detailed example.