Write the dataset card of v2

README.md

@@ -1,5 +1,5 @@
 ---
-license: cc-by-4.0
+license: mit
 pretty_name: samplingdb reference sets
 tags:
 - sampling
@@ -10,87 +10,19 @@ tags:
 - statistical-physics
 size_categories:
 - 100K<n<1M
-configs:
-- config_name: dw4
-  data_files:
-  - split: test
-    path: viewer/dw4_6b739c6d.parquet
-- config_name: lj13
-  data_files:
-  - split: test
-    path: viewer/lj13_6a0a72c4.parquet
-- config_name: lj55
-  data_files:
-  - split: test
-    path: viewer/lj55_dc3f6b7e.parquet
-- config_name: seeds
-  data_files:
-  - split: test
-    path: viewer/seeds_f9a50f20.parquet
-- config_name: brownian
-  data_files:
-  - split: test
-    path: viewer/brownian_45794097.parquet
-- config_name: lgcp
-  data_files:
-  - split: test
-    path: viewer/lgcp_cea6d9a6.parquet
-- config_name: lgcp-whitened
-  data_files:
-  - split: test
-    path: viewer/lgcp-whitened_30e98363.parquet
-- config_name: breast-cancer
-  data_files:
-  - split: test
-    path: viewer/breast-cancer_543f5f94.parquet
-- config_name: german-credit
-  data_files:
-  - split: test
-    path: viewer/german-credit_3cfdf175.parquet
-- config_name: ionosphere
-  data_files:
-  - split: test
-    path: viewer/ionosphere_d00109c3.parquet
-- config_name: sonar
-  data_files:
-  - split: test
-    path: viewer/sonar_52aa10ef.parquet
-- config_name: trialanine
-  data_files:
-  - split: test
-    path: viewer/aaa-T310_e0bfbee3.parquet
-- config_name: hexaalanine
-  data_files:
-  - split: test
-    path: viewer/aaaaaa-T310_08d2d916.parquet
-- config_name: aldp
-  data_files:
-  - split: test
-    path: viewer/ace-a-nme-T300_54968005.parquet
-- config_name: ala4
-  data_files:
-  - split: test
-    path: viewer/ace-aaa-nme-T300_cda31701.parquet
-- config_name: ka-ternary-T0.32
-  data_files:
-  - split: test
-    path: viewer/ka-ternary-N44-T0.32_d50e8a3f.parquet
-- config_name: ka-ternary-T1.0
-  data_files:
-  - split: test
-    path: viewer/ka-ternary-N44-T1.0_c0f9b3b8.parquet
+viewer: false
 ---
 
 # samplingdb reference sets
 
 Reference samples for the targets of [samplingdb](https://github.com/h2o64/samplingdb), a PyTorch
 library of unnormalised densities for benchmarking samplers. A sampler's output is scored against
-these sets. Each one comes from a long MCMC run on the target's own density, and was checked for
-convergence and independence before it was published here.
+these sets. Each one comes from a long Markov chain run on the target's own density (MCMC, or
+replica-exchange molecular dynamics for the peptides), or from an exact sampler where the posterior
+allows one, and passed the convergence and independence checks below before it was published here.
 
 You do not need to download anything by hand. samplingdb fetches a set the first time it is used,
-checks its sha256, and keeps it in its cache. To look at a set first, open the dataset viewer above
-and pick the target in the subset menu.
+checks its sha256, and keeps it in its cache.
 
 ## Quick start
 
@@ -111,7 +43,7 @@ To fetch sets ahead of time, for example before a job on machines with no intern
 ```python
 import samplingdb
 
-samplingdb.download_references(['lj55', 'aldp'])   # by catalog name
+samplingdb.download_references(['lj55', 'ace-a-nme-T300'])   # by catalog name
 samplingdb.download_references()                   # every published set, 3.1 GB
 ```
 
@@ -119,7 +51,7 @@ The cache is `~/.cache/samplingdb`, or the directory `SAMPLINGDB_CACHE` names. I
 this repo, so a copy of the repo is a ready cache:
 
 ```sh
-hf download h2o64/samplingdb --repo-type dataset --revision v1 --local-dir "$SAMPLINGDB_CACHE"
+hf download h2o64/samplingdb --repo-type dataset --revision v2 --local-dir "$SAMPLINGDB_CACHE"
 ```
 
 Without samplingdb, every file is a plain NumPy array:
@@ -128,31 +60,10 @@ Without samplingdb, every file is a plain NumPy array:
 import numpy as np
 from huggingface_hub import hf_hub_download
 
-path = hf_hub_download('h2o64/samplingdb', 'pt/dw4_6b739c6d.npy', repo_type='dataset', revision='v1')
+path = hf_hub_download('h2o64/samplingdb', 'pt/dw4_d9994289.npy', repo_type='dataset', revision='v2')
 x = np.load(path)   # (n_samples, dim)
 ```
 
-## Browsing, and loading with `datasets`
-
-Each set also has a Parquet copy in `viewer/`, which is what the dataset viewer shows: one subset
-per target, named as in `samplingdb.build`, with one row per sample. The column `x` is the sample
-and `log_prob` is the samplingdb target's unnormalised log density at it, whose histogram the viewer
-draws above the column. For the particle systems, the peptides and the glass, `log_prob` is minus
-the energy in units of kT, so its histogram is the energy distribution, mirrored.
-
-The same files load with the `datasets` library, under the split `test`:
-
-```python
-from datasets import load_dataset
-
-lj55 = load_dataset('h2o64/samplingdb', 'lj55', split='test')
-rows = lj55.with_format('numpy')[:]
-x, log_prob = rows['x'], rows['log_prob']   # (100352, 165) and (100352,)
-```
-
-The `.npy` files are what samplingdb reads and what the tag pins; the Parquet copies hold the same
-rows.
-
 ## Files
 
 Each row of an array is one sample, flattened, in the coordinates the samplingdb target's
@@ -161,9 +72,11 @@ Each row of an array is one sample, flattened, in the coordinates the samplingdb
 | Folder | Targets | Coordinates |
 | --- | --- | --- |
 | `pt/` | DW-4, LJ-13, LJ-55 | particle positions; samplingdb removes the centre of mass when it reads them |
+| `pt/` | binary Kob-Andersen glass | particle positions in the periodic box, species in canonical order |
 | `swapmc/` | ternary Kob-Andersen glass | particle positions in the periodic box, species in canonical order |
 | `md/` | peptides | atom positions in nanometres, in the atom order of the released PDB |
 | `stan/` | Bayesian posteriors | the unconstrained parameters, in samplingdb's order |
+| `exact/` | the Brownian motion posterior | the unconstrained parameters, in samplingdb's order |
 
 A file is named `<folder>/<target name>_<spec_id>.npy`. The `spec_id` is a hash of the constants
 that define the density, so a set can never be read against a different density than the one it
@@ -172,28 +85,29 @@ the full generation protocol with its diagnostics, and the array's sha256.
 
 | File | `build` name | Samples | dim | dtype | Size | Independence |
 | --- | --- | --- | --- | --- | --- | --- |
-| `pt/dw4_6b739c6d.npy` | `dw4` | 102400 | 8 | float32 | 3.3 MB | 91% effective |
-| `pt/lj13_6a0a72c4.npy` | `lj13` | 102400 | 39 | float32 | 16.0 MB | 98% effective |
-| `pt/lj55_dc3f6b7e.npy` | `lj55` | 100352 | 165 | float32 | 66.2 MB | 100% effective |
+| `pt/dw4_d9994289.npy` | `dw4` | 102400 | 8 | float32 | 3.3 MB | 91% effective |
+| `pt/lj13_bd3bc3a0.npy` | `lj13` | 102400 | 39 | float32 | 16.0 MB | 98% effective |
+| `pt/lj55_01c56181.npy` | `lj55` | 100352 | 165 | float32 | 66.2 MB | 100% effective |
+| `pt/ka-binary-N64-T0.2_e60ecba4.npy` | `ka-binary-N64-T0.2` | 30720 | 128 | float32 | 15.7 MB | 91% effective |
 | `stan/seeds_f9a50f20.npy` | `seeds` | 100000 | 26 | float64 | 20.8 MB | bulk ESS 96% of the draws |
-| `stan/brownian_45794097.npy` | `brownian` | 100000 | 32 | float64 | 25.6 MB | bulk ESS 92% of the draws |
+| `exact/brownian_45794097.npy` | `brownian` | 100000 | 32 | float64 | 25.6 MB | exact independent draws |
 | `stan/lgcp_cea6d9a6.npy` | `lgcp` | 100000 | 1600 | float64 | 1280.0 MB | bulk ESS 90% of the draws |
 | `stan/lgcp-whitened_30e98363.npy` | `lgcp-whitened` | 100000 | 1600 | float64 | 1280.0 MB | bulk ESS 98% of the draws |
 | `stan/breast-cancer_543f5f94.npy` | `breast-cancer` | 100000 | 31 | float64 | 24.8 MB | bulk ESS 102% of the draws |
 | `stan/german-credit_3cfdf175.npy` | `german-credit` | 100000 | 25 | float64 | 20.0 MB | bulk ESS 110% of the draws |
 | `stan/ionosphere_d00109c3.npy` | `ionosphere` | 100000 | 35 | float64 | 28.0 MB | bulk ESS 96% of the draws |
 | `stan/sonar_52aa10ef.npy` | `sonar` | 100000 | 61 | float64 | 48.8 MB | bulk ESS 108% of the draws |
-| `md/aaa-T310_e0bfbee3.npy` | `trialanine` | 100000 | 99 | float32 | 39.6 MB | 92% effective |
-| `md/aaaaaa-T310_08d2d916.npy` | `hexaalanine` | 100005 | 189 | float32 | 75.6 MB | 98% effective |
-| `md/ace-a-nme-T300_54968005.npy` | `aldp` | 100000 | 66 | float32 | 26.4 MB | 96% effective |
-| `md/ace-aaa-nme-T300_cda31701.npy` | `ala4` | 100000 | 126 | float32 | 50.4 MB | 94% effective |
-| `swapmc/ka-ternary-N44-T0.32_d50e8a3f.npy` | `ka-ternary-T0.32` | 105600 | 88 | float32 | 37.2 MB | 20 relaxation times apart |
-| `swapmc/ka-ternary-N44-T1.0_c0f9b3b8.npy` | `ka-ternary-T1.0` | 102400 | 88 | float32 | 36.0 MB | 20 relaxation times apart |
+| `md/aaa-T310_e0bfbee3.npy` | `aaa-T310` | 100000 | 99 | float32 | 39.6 MB | 92% effective |
+| `md/aaaaaa-T310_08d2d916.npy` | `aaaaaa-T310` | 100005 | 189 | float32 | 75.6 MB | 98% effective |
+| `md/ace-a-nme-T300_54968005.npy` | `ace-a-nme-T300` | 100000 | 66 | float32 | 26.4 MB | 96% effective |
+| `md/ace-aaa-nme-T300_cda31701.npy` | `ace-aaa-nme-T300` | 100000 | 126 | float32 | 50.4 MB | 94% effective |
+| `swapmc/ka-ternary-N44-T0.32_d50e8a3f.npy` | `ka-ternary-N44-T0.32` | 131072 | 88 | float32 | 46.1 MB | 93% effective |
+| `swapmc/ka-ternary-N44-T1.0_c0f9b3b8.npy` | `ka-ternary-N44-T1.0` | 163840 | 88 | float32 | 57.7 MB | 92% effective |
 
 Independence is the effective sample size as a fraction of the rows: measured on the stored chains
-for the tempered runs, and Stan's smallest bulk ESS for the posteriors, which exceeds 100% where
-successive NUTS draws are anticorrelated. For the glass it is the spacing between stored
-configurations of one chain, in structural relaxation times.
+for the tempered runs and the glass, and Stan's smallest bulk ESS for the posteriors, which exceeds
+100% where successive NUTS draws are anticorrelated. The exact draws are independent by
+construction.
 
 The rows are interleaved across chains, so the first `n` rows of a file span every chain of the run
 that produced it, and `test_samples(n=...)` can read a prefix.
@@ -205,35 +119,28 @@ documents each protocol and its acceptance checks in `docs/reference-sets.md`.
 
 | Folder | Sampler | Checked before writing |
 | --- | --- | --- |
-| `pt/` | non-reversible parallel tempering with HMC moves | every chain makes 10 round trips and every pair of adjacent levels accepts at least 5% of its swaps; the merged set is at least 90% independent, with no drift from its start |
-| `md/` | replica-exchange molecular dynamics with solute tempering, BAOAB at 1 fs | every replica makes a round trip and every pair of adjacent levels accepts at least 5% of its swaps; the merged set is at least 90% independent on the pair distances and backbone torsions, with no drift from its start |
-| `swapmc/` | swap Monte Carlo: 100 relaxation times of equilibration, then one configuration every 20 | consecutive configurations of a chain have decorrelated, and the specific heat agrees between the two halves of the run |
+| `pt/` | non-reversible parallel tempering with HMC moves | every chain makes 10 round trips and every pair of adjacent levels accepts at least 5% of its swaps; the set is at least 90% independent; the binary glass, merged from shards, also shows no drift from its start, and its chains started from deep configurations and from a melt agree within 4 standard errors |
+| `md/` | replica-exchange molecular dynamics with solute tempering, BAOAB at 1 fs | every replica makes a round trip and every pair of adjacent levels accepts at least 5% of its swaps; each shard's energy agrees between the two halves of its run within 5 standard errors; the merged set is at least 90% independent on the pair distances and backbone torsions. The integrator is unadjusted: at 1 fs it puts Ace-A-Nme's mean energy about 0.1 kT above the zero-timestep limit |
+| `swapmc/` | swap Monte Carlo on the GPU, 16 384 chains started from equilibrated configurations and stored at a fixed spacing | the merged set is at least 90% independent on the energy and the pair distances, with no drift from its start, and the specific heat agrees between the two halves of the run |
 | `stan/` | Stan's NUTS, on models written in samplingdb's own coordinates | Stan's log density equals samplingdb's to 1e-9; R-hat below 1.01, bulk ESS above 90% of the draws, no divergences |
-
-## Not published yet
-
-Two catalogued targets have no set in this release. A later release will add them under a new tag.
-
-- `ka-binary-T0.2`, the binary Kob-Andersen glass of Cheng et al. 2026. Its parallel-tempering
-  replicas that crystallise stay trapped at intermediate temperatures, and the set generated so far
-  is not stationary.
-- `chignolin`. Its folding equilibrium is expensive to sample, and the planned 10 000-structure
-  set has not been run yet.
+| `exact/` | rejection sampling of the two log scales from their priors against the bounded marginal likelihood, then the locations from their exact Gaussian conditional | the factorised density equals samplingdb's up to a constant, to 1e-8 across 1000 random points; every proposal stays below the bound |
 
 ## Versions
 
-samplingdb pins one tag of this repo and the sha256 of every file it reads. A release adds files
-under a new tag and never replaces a published one, so every samplingdb version keeps reading the
-exact bytes it was tested against. A density change gets a new `spec_id`, and therefore a new file.
+samplingdb pins one tag of this repo and the sha256 of every file it reads, so every samplingdb
+version keeps reading the exact bytes it was tested against. A density change gets a new
+`spec_id`, and therefore a new file. A set regenerated for the same density keeps its file name and
+changes its bytes under a new tag; samplingdb refuses a cached copy from another tag.
 
 | Tag | Contents |
 | --- | --- |
-| `v1` | the 17 sets above |
+| `v1` | 17 sets |
+| `v2` | the Brownian posterior from an exact sampler, under `exact/` in place of `stan/`; both ternary glass sets regenerated on the GPU; the three particle sets, unchanged, under new names, since the particle count joined their `spec_id`; the binary Kob-Andersen glass at T = 0.2, published for the first time; every set under the MIT License, in place of CC BY 4.0 |
 
 ## License and attribution
 
-The samples are released under CC BY 4.0. They are draws from densities that others defined, so
-please also credit the sources of the densities you use:
+The samples are released under the MIT License, like samplingdb. They are draws from densities
+that others defined, so please also credit the sources of the densities you use:
 
 - DW-4, LJ-13 and LJ-55: the potentials of Köhler et al. 2020, as implemented in bgflow.
 - The logistic regressions: the Ionosphere, Sonar, Statlog (German Credit) and Breast Cancer
@@ -244,3 +151,5 @@ please also credit the sources of the densities you use:
   Amber ff99SB-ILDN and ff14SB force fields and OBC implicit solvent, evaluated with OpenMM.
 - The ternary glass: the swap-friendly Kob-Andersen mixture of Jung et al. 2023, after Kob and
   Andersen 1995.
+- The binary glass: the two-dimensional 80:20 Kob-Andersen mixture of Cheng et al. 2026, after Kob
+  and Andersen 1995.