splits/README.md

WindsorML dataset splits

This directory provides deterministic train/validation/test assignments for the WindsorML dataset. The authoritative assignments are stored in manifest.json as a flat JSON object. Keys follow the pattern {split_name}_{train,val,test}, and each value is a numerically sorted list of identifiers matching the top-level run_N convention.

The aggregate WindsorML tables describe 355 Windsor-body variants, indexed from run_0 through run_354. At the source revision used to construct this package, per-run STL and image assets were available for run_0 through run_349; the treatment of the five remaining cases is documented below.

Splits at a glance

Split	Type	Train	Validation	Test	Intended evaluation
full	In-distribution	284	35	36	Seed-42 random baseline, approximately 80/10/10
medium	In-distribution	95	35	36	Intermediate data efficiency
scarce	In-distribution	47	35	36	Low-data evaluation
super_scarce	In-distribution	8	35	36	Extreme low-data evaluation
geometry	OOD	248	36	71	STL-surface geometry extrapolation
high_drag	OOD	248	36	71	High-drag extrapolation
low_drag	OOD	248	36	71	Low-drag extrapolation
image_wake	OOD	248	36	71	Image-derived wake extrapolation

The data-efficiency training sets form a strict nested sequence:

super_scarce_train ⊂ scarce_train ⊂ medium_train ⊂ full_train

They use the same validation and test assignments. For every out-of-distribution (OOD) family, validation is sampled from the training-side population; the held-out extreme is reserved for final testing.

Relation to the paper split

The WindsorML paper reports a 60/20/20 partition for its preliminary machine- learning evaluation but does not publish the case-membership lists. The full family here is therefore a separate, reproducible seed-42 benchmark with an approximately 80/10/10 ratio. It should not be described as a reconstruction of the paper's preliminary partition.

Using the committed manifest

Normal benchmark use requires only the committed manifest. Regenerating the splits is not required.

import json
from pathlib import Path

manifest = json.loads(Path("splits/manifest.json").read_text())

train_ids = manifest["geometry_train"]
val_ids = manifest["geometry_val"]
test_ids = manifest["geometry_test"]

Change the geometry prefix to full, medium, scarce, super_scarce, high_drag, low_drag, or image_wake to select another family.

Download only the split package with:

hf download neashton/windsorml \
  --type dataset \
  --include "splits/**" \
  --local-dir ./windsorml

Validation data may be used for model and hyperparameter selection. Test data should be reserved for final evaluation and should not inform normalization, feature design, or repeated visual inspection during development.

Construction principles

1. Reproducible baseline. The full family is a committed seed-42 random assignment over all 355 identifiers.
2. In-distribution validation. OOD validation cases are selected from the training-side population rather than the extreme test region.
3. Nested data-efficiency subsets. Smaller training sets are strict subsets of larger sets, with validation and test held fixed.
4. Direct geometry comparison. The geometry OOD score is computed from STL surfaces rather than inferred only from geometry parameters.
5. Dataset-defined physical quantities. Drag families use the published constant-reference-area force table.
6. Flow-structure information. The image-wake family uses fixed velocity views rather than an integrated coefficient.
7. Explicit missing-data treatment. Observed and estimated metric values are identified in the distributed CSVs.
8. Auditability. The manifest is distributed with the derived metrics, scripts, figures, LaTeX source, and PDF methods report used to document it.

Split definitions

full

The baseline constructs torch.randperm(355) with seed 42, assigns the first 284 entries to training, the next 35 to validation, and the final 36 to testing, then sorts each stored list numerically. The identifiers are committed directly in the generator, so PyTorch is not a runtime dependency.

medium, scarce, and super_scarce

These families retain full_val and full_test while reducing the training population to 95, 47, and 8 cases. A greedy max-min procedure constructs one nested ordering in standardized force/geometry feature space using cd, cl, and the columns present in the aggregate geometry table.

The paper defines seven CAD variables. The current root-level geo_parameters_all.csv contains six of those variables plus frontal_area; ratio_length_front_rear is present in per-run geometry CSVs but absent from the aggregate table. The nested selection uses the aggregate columns actually available. The STL-based geometry OOD family is independent of this omission.

geometry

The geometry family uses chamfer_metrics.csv. Each available run_N/windsor_N.stl surface is sampled with 4,096 deterministic area-weighted points. Point clouds remain in the shared dataset coordinate frame and are scaled by the global median STL bounding-box diagonal. Pairwise surface difference is measured with symmetric Chamfer RMS distance.

For each run, the OOD score is the mean distance to its ten nearest neighbouring geometries. The 71 highest-scoring cases form geometry_test; 36 validation cases are deterministically selected from the complementary population, leaving 248 training cases.

high_drag and low_drag

These families rank all cases by cd from the root-level force_mom_all.csv, whose constant reference area makes the coefficients directly comparable. high_drag holds out the largest 71 values, while low_drag holds out the smallest 71. Validation is sampled from the complementary population in both cases.

image_wake

The image-wake score uses two near-centreline constant-z velocity images and three near-base constant-x images for each observed run. A fixed wake crop and colour/intensity measure estimate the low-speed area in each view. The 71 largest scores form image_wake_test.

Current per-run asset coverage

At WindsorML revision bb721834e681a9a8329c42288c1514d6ce617547, the aggregate force and geometry tables contain all 355 identifiers, but the per-run STL and targeted PNG files for run_350 through run_354 are absent. Their geometry and image-wake scores are estimated from the five nearest observed runs in standardized force/aggregate-geometry space.

chamfer_metrics.csv and image_metrics.csv record an observed flag and the neighbour identifiers used for every estimate. They contain 350 direct observations and five estimated rows each. The committed manifest is the versioned benchmark assignment; future metric revisions should be released explicitly rather than silently changing this manifest.

Reproducibility

The committed manifest.json is the source of truth. The commands below are provided to audit or rebuild the artifacts. They were prepared against the WindsorML revision given above.

Install the lightweight generation and plotting dependencies:

python3 -m pip install numpy matplotlib pillow

From the dataset repository root, download the aggregate source tables and regenerate the manifest and diagnostic plot:

python3 splits/download_hf_inputs.py --output-dir data
python3 splits/generate_splits.py
python3 splits/visualize_splits.py

The commands above use the committed Chamfer and image metrics. To recompute those metrics and recreate the example figures, keep large STL and PNG inputs outside the repository:

ASSET_ROOT=../windsorml_hf_assets

python3 splits/download_hf_inputs.py --output-dir "$ASSET_ROOT" \
  --include-stls --include-wake-images --include-geometry-images \
  --workers 6 --allow-missing

python3 splits/compute_chamfer_splits.py --data-root "$ASSET_ROOT" \
  --output-dir /tmp/windsorml_chamfer_4096 --samples 4096 \
  --workers 16 --sample-workers 2 --runs all --allow-missing

cp /tmp/windsorml_chamfer_4096/chamfer_metrics.csv \
  splits/chamfer_metrics.csv

python3 splits/compute_image_metrics.py --asset-root "$ASSET_ROOT" \
  --data-root "$ASSET_ROOT" --output splits/image_metrics.csv

WINDSORML_DATA_ROOT="$ASSET_ROOT" python3 splits/generate_splits.py
WINDSORML_DATA_ROOT="$ASSET_ROOT" python3 splits/visualize_splits.py
python3 splits/create_example_figures.py --asset-root "$ASSET_ROOT" \
  --force-root "$ASSET_ROOT"

Full Chamfer recomputation additionally requires SciPy and trimesh:

python3 -m pip install scipy trimesh

Rebuild the PDF methods report with:

latexmk -pdf -cd splits/README.tex

The generated manifest remains a flat mapping such as:

{
  "full_train": ["run_0", "run_1"],
  "full_val": ["run_8"],
  "full_test": ["run_7"]
}

The shortened lists above illustrate the format only; use the committed manifest for the complete assignments.