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Learn Configuration

Source code:

  • bff/workflows/configs.py
  • bff/workflows/learn.py
  • bff/bayes/learning.py

Purpose

bff learn runs posterior inference from previously trained surrogate models. The reference observation vectors and effective observation counts are read from the surrogate files themselves, not from the original QoI datasets.

Minimal Example

log: out.log
specs: ../02-training-data/trainset/specs.yaml
models:
  rdf: ../04-train-lgp/models/rdf.lgp
  hb: ../04-train-lgp/models/hb.lgp
mcmc:
  total_steps: 10000
  warmup: 2000
  checkpoint: mcmc-checkpoint.pt
  posterior: posterior.pt
  priors: priors.pt
  restart: false
  device: cuda

Top-Level Keys

  • log Workflow log file.
  • specs Force-field specification file from the trainset stage.
  • models Non-empty mapping from QoI name to trained .lgp model file.
  • mcmc Posterior-sampling settings.

models Keys

Each key under models is the QoI name that should appear in logs and plots. Each value is a path to the corresponding trained .lgp file.

mcmc Keys

  • priors_disttype Prior family, currently defaulting to normal.
  • total_steps Total MCMC steps.
  • warmup Burn-in length.
  • thin Chain thinning factor.
  • progress_stride Logging interval.
  • n_walkers Optional walker count. If omitted, BFF chooses a default.
  • checkpoint Checkpoint file path.
  • posterior Posterior chain output path.
  • priors Prior output path.
  • restart Restart from checkpoint if possible.
  • device Torch device for MCMC.
  • rhat_tol R-hat convergence threshold.
  • ess_min Minimum effective sample size target.
  • include_implicit_charge If true, include the implicit charge in prepared posterior samples.