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61 | 61 | 1 | 2cityblock | uniform_random | uniform_random | [
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... | 0.037562 | 19 |
End of preview. Expand in Data Studio
ToR-OT v2 synthetic optimal-transport corpus
Discrete Kantorovich instances with exact sparse optimal plans, generated by
python -m torot.data.generate from the ToR-OT code release
("Learning Optimal Transport Plans Via Autoregressive Token Regression", NeurIPS 2026).
Each record is one instance:
| field | description |
|---|---|
n, m |
number of source / target bins |
dim |
support dimension (1) |
cost_type |
one of sqeuclidean, euclidean, cityblock, chebyshev, minkowski, canberra |
coords_src, coords_tgt |
bin centres of a regular grid on [0,1], canonical order |
a, b |
marginals (float64, sum to 1); uniformly random weights for the random splits |
plan_rows, plan_cols, plan_vals |
COO of the optimal plan returned by POT's network simplex (ot.emd) |
ot_cost |
optimal cost with the max-normalised cost matrix |
family_src, family_tgt |
uniform_random, or the named density for the OOD evaluation split |
seed |
per-instance seed (instances are fully reproducible) |
The cost matrix is not stored; it is torot.data.cost.cost_matrix(coords_src, coords_tgt, cost_type).
Splits
| split | instances |
|---|---|
train |
2000000 |
val |
5000 |
test_id |
10000 |
test_extrap |
5000 |
test_ood |
1728 |
Generation configuration
{
"n_min": 5,
"n_max": 100,
"extrap_n_min": 120,
"extrap_n_max": 200,
"equal_sizes": true,
"cost_types": [
"sqeuclidean",
"euclidean",
"cityblock",
"chebyshev",
"minkowski",
"canberra"
],
"ood_sizes": [
30,
100
]
}
Command:
python -m torot.data.generate --out_dir ./otdata_v2 --train 2000000 --val 5000 --test_id 10000 --test_extrap 5000 --seed 0 --workers 12 --repo_id ivanjaenm/torot_data_2M --push
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