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Jammy flow integration #728
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Jammy flow integration #728
RasmusOrsoe
wants to merge
32
commits into
graphnet-team:main
from
RasmusOrsoe:jammy_flow_integration
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This PR adds support for Normalizing Flows via the jammy_flows package, and therefore supersedes #649. The benefit of using
jammy_flows
is that it contains many different normalizing flows, and that we avoid maintaining that code ourselves :-).The package is not listed as a direct dependency but used as an optional support package.
Specifically, this PR introduces the following major changes:
StandardFlowTask
now uses jammy_flows to construct pdfs of any kind that it supports. These pdfs can be both conditional and non-conditional. Conditional flows can be conditioned on latent model output, event-level information or pulse-level information.NormalizingFlow
which work with theStandardFlowTask
. Usage is similar toStandardModel
.examples/04_training/07_train_normalizing_flow.py
has_jammy_flows_package()
is added undergraphnet.utils.imports
to check if its installed, and is used in a few places to make sure that the code runs also for people who choose not to install jammy_flows.Minor changes:
repeat_labels
is added as an argument toGraphDefinition
- ifTrue
, event-level information, .e.genergy
is repeated row-wise to match the number of pulses in the event. This feature was added in this PR to make it possible to build flows that learn pulse-level pdfs conditioned on event-level information.**kwargs
forTrainer
is added forpredict
-methods inEasySyntax
to allow the same level of control overTrainer
arguments as we have for.fit