Evaluation#
Configuration reference for jaqmc hall evaluate.
This page shows the effective defaults for the evaluation workflow preset. Use
--dry-run to see the resolved config for your run, or add
workflow.config.verbose=true to include field descriptions. Evaluation has
only one stage, so stage keys (run.*, sampler.*, writers.*) live at the
config root rather than under a train.* prefix. Defaults are resolved in this
order: schema defaults, workflow preset, YAML config, then CLI overrides. For
training config, see Training.
Root-level runtime keys such as logging.*, jax.*, and distributed.* are
shared by all commands. See Runtime Configuration.
Workflow (workflow.*)#
These keys control evaluation-wide settings and checkpoint loading.
workflow.seed
Fixed random seed.
workflow.batch_size
Number of walkers (samples) to use in each iteration.
workflow.save_path
Path to save checkpoints and logs.
workflow.restore_path
Path to restore checkpoints from.
workflow.config.ignore_extra
If True, silently ignore unrecognized config keys.
workflow.config.verbose
If True, print the fully resolved config with field descriptions at startup.
workflow.source_path
Path to the training run directory or checkpoint file to load parameters from.
System (system.*)#
Must match the training run. The effective defaults are identical to the training system config.
Wavefunction (wf.*)#
Must match the training run. The effective defaults and built-in module choices are identical to the training wavefunction config.
Run Options (run.*)#
Evaluation reuses the same checkpointing and sampling controls as training, but
adds digest_step_interval for previewing accumulated statistics.
run.check_vma
Enable JAX validity checks during shard_map.
run.iterations
Total number of iterations to run.
run.burn_in
Sampling iterations to discard before the main loop for MCMC equilibration.
run.save_time_interval
Minimum wall-clock seconds between checkpoint saves.
run.save_step_interval
Save checkpoints only at steps that are multiples of this value.
run.digest_step_interval
Log a preview of the accumulated evaluation digest every this many steps.
Sampler (sampler.*)#
Default sampler module:
mcmc, and its effective keys are listed below.
sampler.steps
Number of Metropolis-Hastings updates per sample draw.
sampler.initial_width
Initial width (stddev) of the Gaussian proposal.
sampler.adapt_frequency
Frequency of adaptive width updates.
sampler.pmove_range
Target range for acceptance rate.
Writers (writers.*)#
No external writers are enabled by default. If you enable them manually, the root-level writer keys below control their configuration. The evaluation stage always writes per-step statistics to an internal HDF5 file for digest computation; this is independent of the writers configured here.
Console writer (writers.console.*)#
writers.console.interval
Step interval for logging.
writers.console.fields
Comma-separated list of field specs.
CSV writer (writers.csv.*)#
writers.csv.path_template
Output path template.
HDF5 writer (writers.hdf5.*)#
writers.hdf5.path_template
Output path template.
Estimators (estimators.*)#
Energy estimator definitions match training, with additional evaluation-only estimators enabled through boolean flags.
TotalEnergyis added automatically by the workflow and is not configurable via a config key.When
system.lz_penaltyorsystem.l2_penaltyare nonzero, aPenalizedLossestimator is added automatically.estimators.enabled.energydefaults totrue.estimators.enabled.densitydefaults tofalse.estimators.enabled.pair_correlationdefaults tofalse.estimators.enabled.one_rdmdefaults tofalse.
Kinetic energy (estimators.energy.kinetic.*)#
estimators.energy.kinetic.vmap_chunk_size
Number of walkers to evaluate per vmap chunk.
estimators.energy.kinetic.mode
Laplacian computation mode. scan and fori_loop use a Hessian-based approach; forward_laplacian uses the forward Laplacian.
estimators.energy.kinetic.monopole_strength
Half the magnetic flux (\(Q = \text{flux}/2\)).
estimators.energy.kinetic.radius
Sphere radius.
Coulomb potential (estimators.energy.potential.*)#
estimators.energy.potential.vmap_chunk_size
Number of walkers to evaluate per vmap chunk.
estimators.energy.potential.interaction_type
Interaction potential form.
estimators.energy.potential.monopole_strength
\(Q = \text{flux}/2\).
estimators.energy.potential.radius
Sphere radius.
estimators.energy.potential.interaction_strength
Overall scaling factor.
Density (estimators.density.*)#
Accumulates a histogram of the polar angle \(\theta\) to measure electron density on the sphere.
estimators.density.bins_theta
Number of bins for the polar angle.
estimators.density.bins_phi
Number of bins for the azimuthal angle. None (default) produces a 1-D theta-only histogram.
Pair correlation (estimators.pair_correlation.*)#
Computes the pair correlation function \(g(\theta)\) on the Haldane sphere.
estimators.pair_correlation.bins
Number of histogram bins.
One-body RDM (estimators.one_rdm.*)#
Computes the one-body reduced density matrix in the monopole harmonic basis.
estimators.one_rdm.vmap_chunk_size
Number of walkers to evaluate per vmap chunk.
estimators.one_rdm.flux
Magnetic flux \(2Q\) (positive integer).