| --- |
| 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 |
|
|
| ```json |
| { |
| "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 |
| ``` |
|
|
