Write the dataset card of v3

README.md

@@ -1,6 +1,6 @@
 ---
 license: mit
-pretty_name: samplingdb reference sets
+pretty_name: samplingdb reference sets and train splits
 tags:
 - sampling
 - mcmc
@@ -20,6 +20,8 @@ library of unnormalised densities for benchmarking samplers. A sampler's output
 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.
+The repo also holds every target's default train split, the cheap and biased set a sampler may
+learn from, so that every user reads the same one.
 
 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.
@@ -35,6 +37,7 @@ from samplingdb import build
 
 target = build('lj55')
 reference = target.test_samples(n=10000)   # downloads the set once (66 MB), then reads the cache
+train = target.train_samples()             # the published train split, downloaded once
 metrics = target.compute_metrics(samples)  # samples: your sampler's output, same shape
 ```
 
@@ -44,14 +47,14 @@ To fetch sets ahead of time, for example before a job on machines with no intern
 import samplingdb
 
 samplingdb.download_references(['lj55', 'ace-a-nme-T300'])   # by catalog name
-samplingdb.download_references()                   # every published set, 3.1 GB
+samplingdb.download_references()                   # every published set, 6.3 GB
 ```
 
 The cache is `~/.cache/samplingdb`, or the directory `SAMPLINGDB_CACHE` names. It is laid out like
 this repo, so a copy of the repo is a ready cache:
 
 ```sh
-hf download h2o64/samplingdb --repo-type dataset --revision v2 --local-dir "$SAMPLINGDB_CACHE"
+hf download h2o64/samplingdb --repo-type dataset --revision v3 --local-dir "$SAMPLINGDB_CACHE"
 ```
 
 Without samplingdb, every file is a plain NumPy array:
@@ -60,7 +63,7 @@ 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_d9994289.npy', repo_type='dataset', revision='v2')
+path = hf_hub_download('h2o64/samplingdb', 'pt/dw4_d9994289.npy', repo_type='dataset', revision='v3')
 x = np.load(path)   # (n_samples, dim)
 ```
 
@@ -77,8 +80,10 @@ Each row of an array is one sample, flattened, in the coordinates the samplingdb
 | `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 |
+| `train/` | every target | the coordinates of the target's own folder above, or its `log_prob`'s |
 
-A file is named `<folder>/<target name>_<spec_id>.npy`. The `spec_id` is a hash of the constants
+A file is named `<folder>/<target name>_<spec_id>.npy`, and a train split
+`train/<target name>_<spec_id>_<recipe>.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
 was drawn from. Next to every array, `<same name>_manifest.json` records the target, its `spec_id`,
 the full generation protocol with its diagnostics, and the array's sha256.
@@ -103,11 +108,39 @@ the full generation protocol with its diagnostics, and the array's sha256.
 | `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 |
+| `train/funnel_556b9f8f_uniform-neck.npy` | `funnel` | 100000 | 10 | float64 | 8.0 MB | train split, recipe uniform-neck |
+| `train/manywell_30beb4d2_uniform-sides.npy` | `manywell` | 100000 | 32 | float64 | 25.6 MB | train split, recipe uniform-sides |
+| `train/gmm40_a1f669cb_skewed-modes.npy` | `gmm40` | 100000 | 2 | float64 | 1.6 MB | train split, recipe skewed-modes |
+| `train/mos_3d819321_skewed-modes.npy` | `mos` | 100000 | 2 | float64 | 1.6 MB | train split, recipe skewed-modes |
+| `train/twomodes_0eddf6c9_skewed-modes.npy` | `twomodes` | 100000 | 2 | float64 | 1.6 MB | train split, recipe skewed-modes |
+| `train/brackettwomodes_334746c8_skewed-modes.npy` | `brackettwomodes` | 100000 | 2 | float64 | 1.6 MB | train split, recipe skewed-modes |
+| `train/manymodes-k3_edf2fcb2_skewed-modes.npy` | `manymodes-k3` | 100000 | 2 | float64 | 1.6 MB | train split, recipe skewed-modes |
+| `train/eightgaussians_ae0110e3_skewed-modes.npy` | `eightgaussians` | 100000 | 2 | float64 | 1.6 MB | train split, recipe skewed-modes |
+| `train/rings_5deb01ff_skewed-modes.npy` | `rings` | 100000 | 2 | float64 | 1.6 MB | train split, recipe skewed-modes |
+| `train/checkerboard_9547623f_skewed-modes.npy` | `checkerboard` | 100000 | 2 | float64 | 1.6 MB | train split, recipe skewed-modes |
+| `train/dw4_d9994289_tempered-mala.npy` | `dw4` | 100000 | 8 | float32 | 3.2 MB | train split, recipe tempered-mala |
+| `train/lj13_bd3bc3a0_tempered-mala.npy` | `lj13` | 100000 | 39 | float32 | 15.6 MB | train split, recipe tempered-mala |
+| `train/lj55_01c56181_tempered-mala.npy` | `lj55` | 100000 | 165 | float32 | 66.0 MB | train split, recipe tempered-mala |
+| `train/seeds_f9a50f20_tempered-mala.npy` | `seeds` | 100000 | 26 | float64 | 20.8 MB | train split, recipe tempered-mala |
+| `train/brownian_45794097_tempered-mala.npy` | `brownian` | 100000 | 32 | float64 | 25.6 MB | train split, recipe tempered-mala |
+| `train/lgcp_cea6d9a6_tempered-mala.npy` | `lgcp` | 100000 | 1600 | float64 | 1280.0 MB | train split, recipe tempered-mala |
+| `train/lgcp-whitened_30e98363_tempered-mala.npy` | `lgcp-whitened` | 100000 | 1600 | float64 | 1280.0 MB | train split, recipe tempered-mala |
+| `train/ka-ternary-N44-T1.0_c0f9b3b8_basin-mc.npy` | `ka-ternary-N44-T1.0` | 100000 | 88 | float32 | 35.2 MB | train split, recipe basin-mc |
+| `train/ka-ternary-N44-T0.32_d50e8a3f_basin-mc.npy` | `ka-ternary-N44-T0.32` | 100000 | 88 | float32 | 35.2 MB | train split, recipe basin-mc |
+| `train/ka-binary-N64-T0.2_e60ecba4_basin-mc.npy` | `ka-binary-N64-T0.2` | 100000 | 128 | float32 | 51.2 MB | train split, recipe basin-mc |
+| `train/ionosphere_d00109c3_tempered-mala.npy` | `ionosphere` | 100000 | 35 | float64 | 28.0 MB | train split, recipe tempered-mala |
+| `train/sonar_52aa10ef_tempered-mala.npy` | `sonar` | 100000 | 61 | float64 | 48.8 MB | train split, recipe tempered-mala |
+| `train/german-credit_3cfdf175_tempered-mala.npy` | `german-credit` | 100000 | 25 | float64 | 20.0 MB | train split, recipe tempered-mala |
+| `train/breast-cancer_543f5f94_tempered-mala.npy` | `breast-cancer` | 100000 | 31 | float64 | 24.8 MB | train split, recipe tempered-mala |
+| `train/ace-a-nme-T300_54968005_basin-md.npy` | `ace-a-nme-T300` | 100000 | 66 | float32 | 26.4 MB | train split, recipe basin-md |
+| `train/ace-aaa-nme-T300_cda31701_basin-md.npy` | `ace-aaa-nme-T300` | 100000 | 126 | float32 | 50.4 MB | train split, recipe basin-md |
+| `train/aaa-T310_e0bfbee3_basin-md.npy` | `aaa-T310` | 100000 | 99 | float32 | 39.6 MB | train split, recipe basin-md |
+| `train/aaaaaa-T310_08d2d916_basin-md.npy` | `aaaaaa-T310` | 100000 | 189 | float32 | 75.6 MB | train split, recipe basin-md |
 
 Independence is the effective sample size as a fraction of the rows: measured on the stored chains
 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.
+construction. A train split is biased on purpose, so its row says which recipe made it.
 
 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.
@@ -124,6 +157,7 @@ documents each protocol and its acceptance checks in `docs/reference-sets.md`.
 | `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 |
 | `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 |
+| `train/` | the target's own `generate_train_samples` at seed 0, 100 000 samples, on a GPU (`gen/train/generate.py`) | samplingdb's tests: every mode of the test split visited in the wrong proportions, and the test energies covered at both ends |
 
 ## Versions
 
@@ -136,6 +170,7 @@ changes its bytes under a new tag; samplingdb refuses a cached copy from another
 | --- | --- |
 | `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 |
+| `v3` | the default train split of every target, under `train/`; the reference sets of `v2`, unchanged |
 
 ## License and attribution