README.md

metadata

dataset_info:
  features:
    - name: 'n'
      dtype: int32
    - name: m
      dtype: int32
    - name: dim
      dtype: int32
    - name: cost_type
      dtype:
        class_label:
          names:
            '0': sqeuclidean
            '1': euclidean
            '2': cityblock
            '3': chebyshev
            '4': minkowski
            '5': canberra
    - name: family_src
      dtype: string
    - name: family_tgt
      dtype: string
    - name: coords_src
      list:
        list: float64
    - name: coords_tgt
      list:
        list: float64
    - name: a
      list: float64
    - name: b
      list: float64
    - name: plan_rows
      list: int32
    - name: plan_cols
      list: int32
    - name: plan_vals
      list: float64
    - name: ot_cost
      dtype: float64
    - name: seed
      dtype: int64
  splits:
    - name: train
      num_bytes: 7726587624
      num_examples: 2000000
    - name: val
      num_bytes: 19319664
      num_examples: 5000
    - name: test_id
      num_bytes: 38642112
      num_examples: 10000
    - name: test_extrap
      num_bytes: 58005664
      num_examples: 5000
    - name: test_ood
      num_bytes: 7863344
      num_examples: 1728
  download_size: 6896350978
  dataset_size: 7850418408
configs:
  - config_name: default
    data_files:
      - split: train
        path: data/train-*
      - split: val
        path: data/val-*
      - split: test_id
        path: data/test_id-*
      - split: test_extrap
        path: data/test_extrap-*
      - split: test_ood
        path: data/test_ood-*

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