mmml diagnose-lc-outliers¶
Inspect learning-curve sweeps for bad seeds and NPZ outliers.
Usage¶
mmml diagnose-lc-outliers --help
Options¶
usage: mmml diagnose-lc-outliers [-h] --eval-root EVAL_ROOT [--dataset DATASET]
[--train-npz TRAIN_NPZ] [--json-out JSON_OUT]
[--structure-plot-out STRUCTURE_PLOT_OUT]
[--structure-indices STRUCTURE_INDICES]
[--structure-reference-index STRUCTURE_REFERENCE_INDEX]
[--max-structures MAX_STRUCTURES]
[--plot-out PLOT_OUT] [--plot-style PLOT_STYLE]
[--spike-relative-factor SPIKE_RELATIVE_FACTOR]
[--spike-jump-factor SPIKE_JUMP_FACTOR]
[--test-z-threshold TEST_Z_THRESHOLD]
[--test-ratio-threshold TEST_RATIO_THRESHOLD]
[--top-samples TOP_SAMPLES]
[--top-spikes TOP_SPIKES] [-q]
Diagnose learning-curve sweep outliers (seeds, spikes, NPZ samples).
Input & configuration:
--dataset DATASET Dataset subdir under eval-root (default: aco)
--structure-plot-out STRUCTURE_PLOT_OUT
Write Kabsch-aligned ASE structure overlay (requires
--train-npz)
--structure-indices STRUCTURE_INDICES
Comma-separated NPZ indices to plot (default: top
suspects + global outliers)
--structure-reference-index STRUCTURE_REFERENCE_INDEX
Reference NPZ index for alignment (default: first
plotted index)
--max-structures MAX_STRUCTURES
Max structures in auto-selected overlay (default: 8)
Output & artifacts:
--json-out JSON_OUT Write full JSON report
--plot-out PLOT_OUT Write summary dashboard PNG
--plot-style PLOT_STYLE
Matplotlib style preset
Diagnostics & safety:
-h, --help show this help message and exit
-q, --quiet Suppress tables; still writes --json-out
Other options:
--eval-root EVAL_ROOT
Sweep eval root (…/learning_curve/e1000)
--train-npz TRAIN_NPZ
Train NPZ for split reproduction + scoring
--spike-relative-factor SPIKE_RELATIVE_FACTOR
--spike-jump-factor SPIKE_JUMP_FACTOR
--test-z-threshold TEST_Z_THRESHOLD
--test-ratio-threshold TEST_RATIO_THRESHOLD
--top-samples TOP_SAMPLES
--top-spikes TOP_SPIKES
Example: mmml diagnose-lc-outliers \ --eval-root out/eval/learning_curve/e1000 \
--dataset aco \ --train-npz out/splits/aco/energies_forces_dipoles_train.npz