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- heldout/ncrna/data_ncrna_train_chunk1.done +1 -0
- heldout/ncrna/data_ncrna_train_chunk1.jsonl +157 -0
- heldout/ncrna/data_ncrna_train_chunk2.done +1 -0
- heldout/ncrna/data_ncrna_train_chunk2.jsonl +181 -0
- heldout/ncrna/data_ncrna_train_chunk3.done +1 -0
- heldout/ncrna/data_ncrna_train_chunk3.jsonl +184 -0
- heldout/ncrna/data_ncrna_train_chunk4.done +1 -0
- heldout/ncrna/data_ncrna_train_chunk4.jsonl +19 -0
- heldout/organelle/data_organelle_train_chunk1.done +1 -0
- heldout/organelle/data_organelle_train_chunk1.jsonl +6 -0
- heldout/selected.json +0 -0
- plan_train_eukaryotic.json +19 -0
- plan_train_rest.json +28 -0
- plan_valid.json +16 -0
- scripts/build.sh +41 -0
- scripts/check_rates.py +37 -0
- scripts/og2baseline.py +120 -0
- scripts/og2dataloader.py +131 -0
- scripts/og2load.py +87 -0
- scripts/og2stats.py +150 -0
- scripts/og2subset.py +397 -0
- scripts/requirements.txt +3 -0
- scripts/species_overlap.py +45 -0
- stats/baseline.json +613 -0
- stats/heldout.json +1611 -0
- stats/train.json +1611 -0
- stats/valid.json +1611 -0
- train/manifest.json +158 -0
- train/selected.json +0 -0
- valid/manifest.json +151 -0
- valid/selected.json +1 -0
README.md
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| 1 |
+
---
|
| 2 |
+
pretty_name: OpenGenome2 12B-token uniform subset
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| 3 |
+
license: apache-2.0 # same as OpenGenome2 (arcinstitute/opengenome2)
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| 4 |
+
tags: [dna, genomics, language-modeling, evo2, opengenome2]
|
| 5 |
+
---
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| 6 |
+
|
| 7 |
+
# og2_small: a 11.783B-token uniform sample of OpenGenome2
|
| 8 |
+
|
| 9 |
+
A small DNA language-modeling dataset with the variety of the full [OpenGenome2](https://huggingface.co/datasets/arcinstitute/opengenome2) corpus used to train Evo 2 (~8.8T tokens): every base of OpenGenome2's training shards was kept with the same probability (1.310e-03), in windows of up to 16,384 bases, so subsets, taxa, GC content and repeats keep their proportions. A second, species-disjoint validation set holds out ~1% of species. Everything is reproducible with the scripts in `scripts/` (same seed, same sample).
|
| 10 |
+
|
| 11 |
+
## Splits
|
| 12 |
+
|
| 13 |
+
| split | tokens | windows | species | contents |
|
| 14 |
+
|---|---|---|---|---|
|
| 15 |
+
| `train` | 11.783B | 1,539,679 | 39,317 | training data: a token-uniform sample of OpenGenome2's train shards, minus the held-out species |
|
| 16 |
+
| `heldout` | 0.124B | 15,940 | 368 | validation, held-out species: every record of ~1% of species (and ~1% of untagged records), sampled at the same rate; no species overlap with train |
|
| 17 |
+
| `valid` | 99.1M | 7,811 | 59 | validation, Evo 2's own split: a sample of OpenGenome2's valid shards, held out by region, not species (31 of its 59 species, 80% of its tagged bases, also occur in train; few species; kept for comparison with Evo 2 numbers) |
|
| 18 |
+
|
| 19 |
+
Tokens are bases (one byte each). Species counts are distinct `S__` tags; only the eukaryotic-genome and GTDB subsets carry tags, so untagged subsets (metagenomes, transcripts, viruses, ...) add sequence variety that is not counted as species.
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| 20 |
+
|
| 21 |
+
## Composition
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| 22 |
+
|
| 23 |
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Share of tokens per OpenGenome2 subset, in the sample and in the full train split (characters streamed), and distinct species per subset.
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| 24 |
+
|
| 25 |
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| subset | train tokens | train share | full share | realized rate / rate | species | heldout tokens |
|
| 26 |
+
|---|---|---|---|---|---|---|
|
| 27 |
+
| ncbi_eukaryotic_genomes | 8.691B | 0.7376 | 0.7447 | 0.964 | 12,058 | 93.8M |
|
| 28 |
+
| metagenomes | 1.110B | 0.0942 | 0.0915 | 1.002 | 0 | 11.0M |
|
| 29 |
+
| eukaryotic_genic_windows | 0.545B | 0.0462 | 0.0450 | 1.000 | 0 | 5.8M |
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| 30 |
+
| gtdb_v220_stitched | 0.463B | 0.0393 | 0.0383 | 0.999 | 27,259 | 4.1M |
|
| 31 |
+
| gtdb_v220_imgpr | 0.461B | 0.0391 | 0.0383 | 0.995 | 0 | 4.5M |
|
| 32 |
+
| mrna_splice_promoter | 0.257B | 0.0218 | 0.0211 | 1.003 | 0 | 2.7M |
|
| 33 |
+
| mrna | 0.151B | 0.0128 | 0.0124 | 1.002 | 0 | 1.5M |
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| 34 |
+
| imgvr_untagged | 46.9M | 0.0040 | 0.0039 | 0.997 | 0 | 0.5M |
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| 35 |
+
| ncrna | 42.7M | 0.0036 | 0.0035 | 1.005 | 0 | 0.4M |
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| 36 |
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| imgpr | 8.0M | 0.0007 | 0.0006 | 1.065 | 0 | 0.1M |
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| 37 |
+
| organelle | 7.9M | 0.0007 | 0.0006 | 1.091 | 0 | 0.1M |
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| 38 |
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| promoters | 0.3M | 0.0000 | 0.0000 | 0.942 | 0 | 0.0M |
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| 39 |
+
|
| 40 |
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Realized rate = kept bases / characters streamed (characters include tag and separator characters, so it is slightly below 1 for tagged subsets). The eukaryotic genomes were sampled with the length-weighted window rule (see How it was built), hence ~0.96.
|
| 41 |
+
|
| 42 |
+
Domains (`D__` of the window's tag) and the most common phyla (`P__`), share of train tokens:
|
| 43 |
+
|
| 44 |
+
| domain | share | | phylum | share |
|
| 45 |
+
|---|---|---|---|---|
|
| 46 |
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| EUKARYOTA | 0.7376 | | CHORDATA | 0.3299 |
|
| 47 |
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| (untagged) | 0.2231 | | (untagged) | 0.2231 |
|
| 48 |
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| BACTERIA | 0.0381 | | STREPTOPHYTA | 0.1876 |
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| 49 |
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| ARCHAEA | 0.0012 | | ARTHROPODA | 0.1557 |
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| 50 |
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| | | | MOLLUSCA | 0.0248 |
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| 51 |
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| | | | PSEUDOMONADOTA | 0.0111 |
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| 52 |
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| | | | ASCOMYCOTA | 0.0099 |
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| 53 |
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| | | | BACTEROIDOTA | 0.0054 |
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| 54 |
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| | | | ACTINOMYCETOTA | 0.0053 |
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| 55 |
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| | | | CNIDARIA | 0.0050 |
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| 56 |
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| | | | BASIDIOMYCOTA | 0.0047 |
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| 57 |
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| | | | ANNELIDA | 0.0043 |
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| 58 |
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| | | | ECHINODERMATA | 0.0041 |
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| 59 |
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| | | | BACILLOTA_A | 0.0038 |
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| 60 |
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| | | | NEMATODA | 0.0033 |
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| 61 |
+
|
| 62 |
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227 phyla in total (counting `(untagged)` as one).
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| 63 |
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|
| 64 |
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## Gross statistics
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| 65 |
+
|
| 66 |
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Computed by `scripts/og2stats.py`. Frequencies are over all bases (incl. N); GC is over A/C/G/T; soft-masked = lowercase (repeat-masked) bases; CpG o/e = f(CG) / (f(C) f(G)).
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| 67 |
+
|
| 68 |
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| split | tokens | A | C | G | T | N | GC | soft-masked | CpG o/e | Chargaff-2 asym. (4-mers) |
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| 69 |
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|---|---|---|---|---|---|---|---|---|---|---|
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| 70 |
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| `train` | 11.783B | 0.2890 | 0.2110 | 0.2110 | 0.2889 | 0.00005 | 0.4221 | 0.3248 | 0.747 | 0.0002 |
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| 71 |
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| `heldout` | 0.124B | 0.2875 | 0.2123 | 0.2121 | 0.2880 | 0.00006 | 0.4245 | 0.3479 | 0.759 | 0.0019 |
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| 72 |
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| `valid` | 99.1M | 0.2776 | 0.2222 | 0.2226 | 0.2776 | 0.00001 | 0.4448 | 0.2533 | 0.935 | 0.0024 |
|
| 73 |
+
|
| 74 |
+
Per subset (train):
|
| 75 |
+
|
| 76 |
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| subset | GC | soft-masked | CpG o/e | h_0 | h_3 | h_7 |
|
| 77 |
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|---|---|---|---|---|---|---|
|
| 78 |
+
| ncbi_eukaryotic_genomes | 0.3951 | 0.4205 | 0.563 | 1.9680 | 1.9434 | 1.9284 |
|
| 79 |
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| metagenomes | 0.5263 | 0.0000 | 1.161 | 1.9980 | 1.9675 | 1.9494 |
|
| 80 |
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| eukaryotic_genic_windows | 0.4113 | 0.3161 | 0.565 | 1.9772 | 1.9483 | 1.9290 |
|
| 81 |
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| gtdb_v220_stitched | 0.5404 | 0.0000 | 1.168 | 1.9953 | 1.9601 | 1.9391 |
|
| 82 |
+
| gtdb_v220_imgpr | 0.5413 | 0.0000 | 1.168 | 1.9951 | 1.9600 | 1.9391 |
|
| 83 |
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| mrna_splice_promoter | 0.4407 | 0.0000 | 0.721 | 1.9901 | 1.9646 | 1.9472 |
|
| 84 |
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| mrna | 0.4641 | 0.0000 | 0.723 | 1.9963 | 1.9688 | 1.9548 |
|
| 85 |
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| imgvr_untagged | 0.4544 | 0.0000 | 1.018 | 1.9940 | 1.9754 | 1.9583 |
|
| 86 |
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| ncrna | 0.4641 | 0.0000 | 0.737 | 1.9962 | 1.9781 | 1.8464 |
|
| 87 |
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| imgpr | 0.5658 | 0.0000 | 1.182 | 1.9875 | 1.9553 | 1.9347 |
|
| 88 |
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| organelle | 0.3724 | 0.0000 | 0.859 | 1.9525 | 1.9299 | 1.8686 |
|
| 89 |
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| promoters | 0.4625 | 0.0000 | 0.844 | 1.9959 | 1.9444 | 1.7395 |
|
| 90 |
+
|
| 91 |
+
Per domain (train):
|
| 92 |
+
|
| 93 |
+
| domain | tokens | GC | soft-masked | CpG o/e |
|
| 94 |
+
|---|---|---|---|---|
|
| 95 |
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| EUKARYOTA | 8.691B | 0.3951 | 0.4205 | 0.563 |
|
| 96 |
+
| (untagged) | 2.628B | 0.4905 | 0.0655 | 1.023 |
|
| 97 |
+
| BACTERIA | 0.449B | 0.5424 | 0.0000 | 1.169 |
|
| 98 |
+
| ARCHAEA | 13.9M | 0.4729 | 0.0000 | 1.086 |
|
| 99 |
+
|
| 100 |
+
### k-mer spectra
|
| 101 |
+
|
| 102 |
+
Markov conditional entropy h_k = H(k+1-mers) - H(k-mers): the bits per base of an order-k Markov model fitted in-sample (case folded; k-mers with N or crossing a window boundary skipped). A trained model should beat h_k for large k on held-out data; h_0 is the entropy of the base composition (2 bits = uniform).
|
| 103 |
+
|
| 104 |
+
| split | h_0 | h_1 | h_2 | h_3 | h_4 | h_5 | h_6 | h_7 | distinct 8-mers (of 65,536) |
|
| 105 |
+
|---|---|---|---|---|---|---|---|---|---|
|
| 106 |
+
| `train` | 1.9824 | 1.9712 | 1.9659 | 1.9602 | 1.9561 | 1.9527 | 1.9459 | 1.9422 | 65,536 |
|
| 107 |
+
| `heldout` | 1.9835 | 1.9728 | 1.9674 | 1.9606 | 1.9548 | 1.9499 | 1.9407 | 1.9344 | 65,536 |
|
| 108 |
+
| `valid` | 1.9912 | 1.9837 | 1.9802 | 1.9705 | 1.9666 | 1.9630 | 1.9551 | 1.9510 | 65,536 |
|
| 109 |
+
|
| 110 |
+
Most frequent 8-mers in train (case folded): `AAAAAAAA` 0.00075, `TTTTTTTT` 0.00074, `ATATATAT` 0.00064, `TATATATA` 0.00063, `TGTGTGTG` 0.00032, `CACACACA` 0.00032, `GTGTGTGT` 0.00032, `ACACACAC` 0.00031, `AAAAAAAT` 0.00026, `ATTTTTTT` 0.00026, `TTTATTTT` 0.00023, `AAAATAAA` 0.00023.
|
| 111 |
+
|
| 112 |
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Full 8-mer counts (overall and per subset) are in `stats/<split>_kmer8.npz` (index = base-4 number, A=0 C=1 G=2 T=3, first base most significant); all numbers above are in `stats/<split>.json`.
|
| 113 |
+
|
| 114 |
+
## Baselines
|
| 115 |
+
|
| 116 |
+
Order-k Markov models fitted on `train` (8-mer counts, add-0.5 smoothing) and scored on each split, in bits per base (`scripts/og2baseline.py`). Scored positions: target and its 7 previous bases all A/C/G/T, the same positions for every order (the loss mask's positions minus the first 7 of each window). Held-out and valid are out of sample; train is in sample (optimistic). A model trained on `train` should be compared with the best order on `heldout`. A subset can score above 2 bits: the models are fitted on the pooled train composition (GC 0.42, mostly eukaryotic) and scored on, e.g., GC-rich bacteria and metagenomes, so the per-subset rows measure how far one global k-mer model is from each kind of sequence. The in-sample train row can exceed the heldout one because the splits' subset mixes differ slightly.
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| 117 |
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|
| 118 |
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| split | positions | k=0 | k=1 | k=2 | k=3 | k=4 | k=5 | k=6 | k=7 | best |
|
| 119 |
+
|---|---|---|---|---|---|---|---|---|---|---|
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| 120 |
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| `heldout` | 124,329,163 | 1.9835 | 1.9728 | 1.9676 | 1.9610 | 1.9560 | 1.9519 | 1.9441 | 1.9399 | 1.9399 (k=7) |
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| 121 |
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| `valid` | 98,998,086 | 1.9927 | 1.9878 | 1.9849 | 1.9762 | 1.9725 | 1.9694 | 1.9622 | 1.9593 | 1.9593 (k=7) |
|
| 122 |
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| `train` | 11,770,630,917 | 1.9824 | 1.9712 | 1.9659 | 1.9602 | 1.9561 | 1.9527 | 1.9459 | 1.9422 | 1.9422 (k=7) |
|
| 123 |
+
|
| 124 |
+
Per subset, `heldout` (best order and its bits/base; order 0 = base composition only):
|
| 125 |
+
|
| 126 |
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| subset | positions | k=0 | k=3 | k=5 | best |
|
| 127 |
+
|---|---|---|---|---|---|
|
| 128 |
+
| ncbi_eukaryotic_genomes | 93,733,341 | 1.9729 | 1.9499 | 1.9406 | 1.9300 (k=7) |
|
| 129 |
+
| metagenomes | 10,972,341 | 2.0286 | 2.0107 | 2.0021 | 1.9844 (k=7) |
|
| 130 |
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| eukaryotic_genic_windows | 5,755,486 | 1.9786 | 1.9525 | 1.9425 | 1.9307 (k=7) |
|
| 131 |
+
| gtdb_v220_imgpr | 4,501,410 | 2.0354 | 2.0153 | 2.0063 | 1.9863 (k=7) |
|
| 132 |
+
| gtdb_v220_stitched | 4,144,567 | 2.0373 | 2.0156 | 2.0050 | 1.9824 (k=7) |
|
| 133 |
+
| mrna_splice_promoter | 2,621,969 | 1.9893 | 1.9657 | 1.9592 | 1.9499 (k=7) |
|
| 134 |
+
| mrna | 1,531,010 | 2.0046 | 1.9801 | 1.9766 | 1.9674 (k=7) |
|
| 135 |
+
| imgvr_untagged | 450,528 | 2.0040 | 1.9955 | 1.9925 | 1.9800 (k=7) |
|
| 136 |
+
| ncrna | 415,009 | 2.0021 | 1.9841 | 1.9802 | 1.9732 (k=7) |
|
| 137 |
+
| imgpr | 102,868 | 2.0515 | 2.0298 | 2.0166 | 1.9952 (k=7) |
|
| 138 |
+
| organelle | 98,262 | 1.9701 | 1.9689 | 1.9677 | 1.9628 (k=1) |
|
| 139 |
+
| promoters | 2,372 | 2.0194 | 1.9847 | 1.9706 | 1.9647 (k=7) |
|
| 140 |
+
|
| 141 |
+
## How it was built
|
| 142 |
+
|
| 143 |
+
1. **Stream, don't store.** Each `json/<phase>/<subset>/*_train_*.jsonl.gz` shard of OpenGenome2 (902 shards, 2.79 TB gzip) is streamed over HTTP and decompressed in memory.
|
| 144 |
+
2. **Segments.** A record's text is split at phylogenetic tags (`|D__...;S__...|`), contig separators (`#`) and window separators (`@`); none of these characters end up in the sequence. Each window carries the tag in force (the record's first tag for sequence before it).
|
| 145 |
+
3. **Uniform per base.** A segment is tiled into windows of 16,384 bases on a fixed grid (the last one shorter). Each window is kept iff u < rate, with u ~ U(0,1) from a generator seeded by (seed, shard, record), whatever its length: every base has inclusion probability `rate`, short sequences (promoters, ncRNA, ...) included. u is stored, so the sample can be shrunk uniformly later (`og2subset.py finalize --target-tokens N` keeps u < rate').
|
| 146 |
+
**Exception: `ncbi_eukaryotic_genomes`** was sampled in a first pass with `--rule length_weighted` (a window of length L kept iff u < rate * L / window). That rule under-samples short windows by L / window; it was found after the first pass, all other subsets were re-streamed with the correct rule, and the eukaryotic genomes kept: their windows are 16,384 bases except the last of each segment (records are ~20 Mb), so the realized rate is ~0.96 x rate, with segment ends (next to tags and contig breaks) slightly under-represented.
|
| 147 |
+
4. **Held-out species.** With `--holdout 0.01`, a record goes to `heldout` instead of `train` when blake2b(species) / 2^64 < 0.01 (untagged records: by record). All records of a species go the same way in every shard and subset.
|
| 148 |
+
5. **Determinism.** Seed 0, rate 1.309946e-03, OpenGenome2 revision `84d2a7e690c8bb395d1f4868822dfa031ea561c6` (pinned). A shard's generator is seeded by blake2b(seed, shard path), a record's by (that, record index): the sample does not depend on worker count or order. Nothing was trimmed.
|
| 149 |
+
|
| 150 |
+
Train passes (`plan_train_*.json`):
|
| 151 |
+
|
| 152 |
+
| plan | rule | subsets | rate |
|
| 153 |
+
|---|---|---|---|
|
| 154 |
+
| `plan_train_eukaryotic.json` | length_weighted | ncbi_eukaryotic_genomes | 1.309946e-03 |
|
| 155 |
+
| `plan_train_rest.json` | per_base | metagenomes, eukaryotic_genic_windows, gtdb_v220_imgpr, gtdb_v220_stitched, mrna, imgvr_untagged, mrna_splice_promoter, ncrna, organelle, imgpr, promoters | 1.309946e-03 |
|
| 156 |
+
|
| 157 |
+
## Files and format
|
| 158 |
+
|
| 159 |
+
```
|
| 160 |
+
README.md this card
|
| 161 |
+
plan_train_*.json, plan_valid.json sampling parameters of each pass
|
| 162 |
+
train/<subset>/<shard>.npy uint8 codes of the kept windows of one OpenGenome2 shard, concatenated
|
| 163 |
+
train/<subset>/<shard>.jsonl one line per window: offset, len, u, tag (taxonomy), rec (record index)
|
| 164 |
+
train/<subset>/<shard>.done per-shard stats (records and characters streamed, kept bases, rate, seed)
|
| 165 |
+
train/selected.json, manifest.json window selection and composition; same layout for heldout/ and valid/
|
| 166 |
+
stats/<split>.json, <split>_kmer8.npz gross statistics and 8-mer counts
|
| 167 |
+
stats/baseline.json k-mer baselines (bits/base per order, split and subset)
|
| 168 |
+
scripts/build.sh exact build recipe (stages: euk rest valid finalize stats baseline)
|
| 169 |
+
scripts/og2subset.py streaming sampler (sample / finalize / report)
|
| 170 |
+
scripts/og2stats.py base composition, GC, soft-masking, CpG, k-mer spectra, Markov entropies
|
| 171 |
+
scripts/og2baseline.py order-0..7 Markov baselines, out of sample
|
| 172 |
+
scripts/check_rates.py realized inclusion rate per subset
|
| 173 |
+
scripts/species_overlap.py species shared between splits (heldout: none; valid: most)
|
| 174 |
+
scripts/og2load.py numpy reader (windows, filters by subset / taxon, random crops)
|
| 175 |
+
scripts/og2dataloader.py PyTorch datasets: RandomCrops (train), EvalTiles (eval), non-ACGT loss mask
|
| 176 |
+
```
|
| 177 |
+
|
| 178 |
+
The data are numpy shards with JSONL indexes, not Parquet: read them with `scripts/og2load.py` or `scripts/og2dataloader.py` (below), not `datasets.load_dataset`. Download with `hf download <repo> --repo-type dataset --local-dir og2_small` (or `snapshot_download`), then point the scripts at that directory.
|
| 179 |
+
|
| 180 |
+
Encoding: one byte per base; low 3 bits A=0, C=1, G=2, T=3, N/other=4; bit 7 (0x80) set for lowercase (soft-masked repeat) bases. `codes & 7` gives the base, `codes >> 7` the mask. Windows never cross a record, contig or tag boundary.
|
| 181 |
+
|
| 182 |
+
### Data loader (PyTorch)
|
| 183 |
+
|
| 184 |
+
`scripts/og2dataloader.py` gives next-base prediction samples. `mask_non_acgt=True` (default) sets the label to -100 and `loss_mask` to False wherever the target is not A/C/G/T (N and other IUPAC codes), so `cross_entropy(..., ignore_index=-100)` skips them; inputs keep N (id 4). `lowercase` flags soft-masked targets for optional reweighting.
|
| 185 |
+
|
| 186 |
+
```python
|
| 187 |
+
import sys; sys.path.insert(0, 'scripts')
|
| 188 |
+
import torch.nn.functional as F
|
| 189 |
+
from og2dataloader import RandomCrops, EvalTiles, make_loader
|
| 190 |
+
train = make_loader(RandomCrops('.', 'train', ctx=4096, seed=0, rc_prob=0.5, mask_non_acgt=True), batch_size=16, num_workers=4)
|
| 191 |
+
heldout = make_loader(EvalTiles('.', 'heldout', ctx=4096), batch_size=16) # every window once, deterministic
|
| 192 |
+
batch = next(iter(train)) # input_ids, labels (-100 = masked), loss_mask, lowercase: [16, 4096]
|
| 193 |
+
loss = F.cross_entropy(logits.flatten(0, 1), batch['labels'].flatten(), ignore_index=-100)
|
| 194 |
+
```
|
| 195 |
+
|
| 196 |
+
Random crops pick a window with probability proportional to its number of possible crops, so every base is equally likely to be trained on; draws are reproducible per (seed, worker). `subset=` and `taxon=` (regex on the tag, e.g. `r'S__HOMO SAPIENS'`, `r'D__ARCHAEA'`) restrict either dataset. Without PyTorch, `scripts/og2load.py` reads windows as numpy arrays.
|
| 197 |
+
|
| 198 |
+
## Reproduce
|
| 199 |
+
|
| 200 |
+
```bash
|
| 201 |
+
pip install -r scripts/requirements.txt
|
| 202 |
+
bash scripts/build.sh # stages: euk rest valid finalize stats baseline (exact commands inside)
|
| 203 |
+
python scripts/check_rates.py --split train # realized inclusion rate per subset
|
| 204 |
+
```
|
| 205 |
+
|
| 206 |
+
The train passes stream 2.8 TB; on 14 CPU cores with ~250 MB/s of bandwidth that takes about 3-4 hours (CPU-bound: decompression and parsing). Only the sample is written (~11 GB). Every stage is resumable.
|
| 207 |
+
|
| 208 |
+
## Caveats
|
| 209 |
+
|
| 210 |
+
- Proportions follow OpenGenome2 as published, including its overlaps: GTDB genomes appear in two subsets (`gtdb_v220_stitched`, midtraining; `gtdb_v220_imgpr`, pretraining), and mRNA in `mrna` and `mrna_splice_promoter`. Evo 2 itself trained on a phase-dependent mixture, not on these raw proportions.
|
| 211 |
+
- About three quarters of the tokens are eukaryotic genomes, dominated by large plant and animal genomes with many repeats (see the soft-masked fraction).
|
| 212 |
+
- Species labels exist only where OpenGenome2 has tags (eukaryotic genomes, GTDB). Held-out untagged records are held out by record, so related sequences (e.g. neighbouring metagenome chunks) can straddle train and heldout.
|
| 213 |
+
- Evo 2's own `valid` split covers few species; prefer `heldout` for measuring generalization.
|
| 214 |
+
|
| 215 |
+
## Source and citation
|
| 216 |
+
|
| 217 |
+
Sequences are from OpenGenome2 (Arc Institute), introduced with Evo 2: Brixi et al., *Genome modeling and design across all domains of life with Evo 2* (2025). Cite the original work.
|
| 218 |
+
|
| 219 |
+
## License
|
| 220 |
+
|
| 221 |
+
Apache 2.0, the license of OpenGenome2 ([arcinstitute/opengenome2](https://huggingface.co/datasets/arcinstitute/opengenome2), revision `84d2a7e690c8bb395d1f4868822dfa031ea561c6`), from which every sequence here is sampled. The scripts in `scripts/` are released under the same license.
|
heldout/manifest.json
ADDED
|
@@ -0,0 +1,158 @@
|
|
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|
|
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|
|
|
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|
|
|
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|
|
|
|
|
|
|
|
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|
|
|
|
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|
|
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|
|
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|
|
|
|
|
|
|
|
|
|
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
{
|
| 2 |
+
"split": "heldout",
|
| 3 |
+
"tokens": 124469222,
|
| 4 |
+
"windows": 15940,
|
| 5 |
+
"selected": true,
|
| 6 |
+
"subsets": {
|
| 7 |
+
"ncbi_eukaryotic_genomes": {
|
| 8 |
+
"tokens": 93795142,
|
| 9 |
+
"windows": 5942,
|
| 10 |
+
"share": 0.7535609244830019,
|
| 11 |
+
"full_share": 0.7595600424333392,
|
| 12 |
+
"records_seen": 3416,
|
| 13 |
+
"chars_seen": 75325631550,
|
| 14 |
+
"species": 125
|
| 15 |
+
},
|
| 16 |
+
"metagenomes": {
|
| 17 |
+
"tokens": 10994038,
|
| 18 |
+
"windows": 2961,
|
| 19 |
+
"share": 0.08832736176337633,
|
| 20 |
+
"full_share": 0.08595491144268257,
|
| 21 |
+
"records_seen": 2128960,
|
| 22 |
+
"chars_seen": 8524155600,
|
| 23 |
+
"species": 0
|
| 24 |
+
},
|
| 25 |
+
"eukaryotic_genic_windows": {
|
| 26 |
+
"tokens": 5759786,
|
| 27 |
+
"windows": 506,
|
| 28 |
+
"share": 0.0462747810860423,
|
| 29 |
+
"full_share": 0.042588696276000725,
|
| 30 |
+
"records_seen": 16421,
|
| 31 |
+
"chars_seen": 4223524494,
|
| 32 |
+
"species": 0
|
| 33 |
+
},
|
| 34 |
+
"gtdb_v220_imgpr": {
|
| 35 |
+
"tokens": 4508717,
|
| 36 |
+
"windows": 421,
|
| 37 |
+
"share": 0.03622354930442162,
|
| 38 |
+
"full_share": 0.03566407954234809,
|
| 39 |
+
"records_seen": 174542,
|
| 40 |
+
"chars_seen": 3536809686,
|
| 41 |
+
"species": 0
|
| 42 |
+
},
|
| 43 |
+
"gtdb_v220_stitched": {
|
| 44 |
+
"tokens": 4148774,
|
| 45 |
+
"windows": 395,
|
| 46 |
+
"share": 0.0333317259747956,
|
| 47 |
+
"full_share": 0.03642542126577604,
|
| 48 |
+
"records_seen": 1158,
|
| 49 |
+
"chars_seen": 3612312007,
|
| 50 |
+
"species": 243
|
| 51 |
+
},
|
| 52 |
+
"mrna_splice_promoter": {
|
| 53 |
+
"tokens": 2653062,
|
| 54 |
+
"windows": 4386,
|
| 55 |
+
"share": 0.021315004282745496,
|
| 56 |
+
"full_share": 0.019897874196785504,
|
| 57 |
+
"records_seen": 569355,
|
| 58 |
+
"chars_seen": 1973273812,
|
| 59 |
+
"species": 0
|
| 60 |
+
},
|
| 61 |
+
"mrna": {
|
| 62 |
+
"tokens": 1536129,
|
| 63 |
+
"windows": 721,
|
| 64 |
+
"share": 0.01234143650387724,
|
| 65 |
+
"full_share": 0.011692235004771378,
|
| 66 |
+
"records_seen": 526658,
|
| 67 |
+
"chars_seen": 1159519902,
|
| 68 |
+
"species": 0
|
| 69 |
+
},
|
| 70 |
+
"imgvr_untagged": {
|
| 71 |
+
"tokens": 450898,
|
| 72 |
+
"windows": 48,
|
| 73 |
+
"share": 0.0036225662276574686,
|
| 74 |
+
"full_share": 0.003662912446567954,
|
| 75 |
+
"records_seen": 26496,
|
| 76 |
+
"chars_seen": 363251327,
|
| 77 |
+
"species": 0
|
| 78 |
+
},
|
| 79 |
+
"ncrna": {
|
| 80 |
+
"tokens": 418990,
|
| 81 |
+
"windows": 541,
|
| 82 |
+
"share": 0.0033662136973909903,
|
| 83 |
+
"full_share": 0.003292699270085657,
|
| 84 |
+
"records_seen": 400519,
|
| 85 |
+
"chars_seen": 326537256,
|
| 86 |
+
"species": 0
|
| 87 |
+
},
|
| 88 |
+
"imgpr": {
|
| 89 |
+
"tokens": 102982,
|
| 90 |
+
"windows": 9,
|
| 91 |
+
"share": 0.0008273691949323825,
|
| 92 |
+
"full_share": 0.0006112749116644716,
|
| 93 |
+
"records_seen": 2101,
|
| 94 |
+
"chars_seen": 60620183,
|
| 95 |
+
"species": 0
|
| 96 |
+
},
|
| 97 |
+
"organelle": {
|
| 98 |
+
"tokens": 98304,
|
| 99 |
+
"windows": 6,
|
| 100 |
+
"share": 0.0007897856065975892,
|
| 101 |
+
"full_share": 0.0006292582889834789,
|
| 102 |
+
"records_seen": 675,
|
| 103 |
+
"chars_seen": 62403596,
|
| 104 |
+
"species": 0
|
| 105 |
+
},
|
| 106 |
+
"promoters": {
|
| 107 |
+
"tokens": 2400,
|
| 108 |
+
"windows": 4,
|
| 109 |
+
"share": 1.9281875161073954e-05,
|
| 110 |
+
"full_share": 2.059492099493525e-05,
|
| 111 |
+
"records_seen": 3404,
|
| 112 |
+
"chars_seen": 2042400,
|
| 113 |
+
"species": 0
|
| 114 |
+
}
|
| 115 |
+
},
|
| 116 |
+
"domains": {
|
| 117 |
+
"EUKARYOTA": 0.7535609244830019,
|
| 118 |
+
"(untagged)": 0.21310734954220248,
|
| 119 |
+
"BACTERIA": 0.032697906635907145,
|
| 120 |
+
"ARCHAEA": 0.0006338193388884523
|
| 121 |
+
},
|
| 122 |
+
"phyla_top30": {
|
| 123 |
+
"STREPTOPHYTA": 0.32169398471856764,
|
| 124 |
+
"CHORDATA": 0.2673104279546312,
|
| 125 |
+
"(untagged)": 0.21310734954220248,
|
| 126 |
+
"ARTHROPODA": 0.1129319905285501,
|
| 127 |
+
"MOLLUSCA": 0.010990909865251669,
|
| 128 |
+
"CNIDARIA": 0.008905848226479635,
|
| 129 |
+
"ACTINOMYCETOTA": 0.006908599460836993,
|
| 130 |
+
"PSEUDOMONADOTA": 0.006747868963140141,
|
| 131 |
+
"ASCOMYCOTA": 0.006502707954581736,
|
| 132 |
+
"NEMATODA": 0.005615179309146802,
|
| 133 |
+
"PLATYHELMINTHES": 0.005400186401100828,
|
| 134 |
+
"BACTEROIDOTA": 0.00531549076445581,
|
| 135 |
+
"ANNELIDA": 0.004535755835285931,
|
| 136 |
+
"BASIDIOMYCOTA": 0.004410206725643388,
|
| 137 |
+
"BACILLOTA_A": 0.004408278538127281,
|
| 138 |
+
"RHODOPHYTA": 0.0033786344386405823,
|
| 139 |
+
"BACILLOTA": 0.00195483667440293,
|
| 140 |
+
"PLANCTOMYCETOTA": 0.000941043883121564,
|
| 141 |
+
"CHLOROPHYTA": 0.0009214165410305208,
|
| 142 |
+
"DEINOCOCCOTA": 0.000898133676773524,
|
| 143 |
+
"CHLOROFLEXOTA": 0.0007345832048343646,
|
| 144 |
+
"EUGLENOZOA": 0.0006581546721646577,
|
| 145 |
+
"VERRUCOMICROBIOTA": 0.0004930777184419132,
|
| 146 |
+
"PATESCIBACTERIA": 0.0004408158026407524,
|
| 147 |
+
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heldout/metagenomes/data_metagenomics_train_chunk1.jsonl
ADDED
|
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heldout/metagenomes/data_metagenomics_train_chunk12.done
ADDED
|
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|
|
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|
|
| 1 |
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heldout/metagenomes/data_metagenomics_train_chunk25.done
ADDED
|
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|
|
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|
|
|
| 1 |
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heldout/metagenomes/data_metagenomics_train_chunk3.jsonl
ADDED
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heldout/metagenomes/data_metagenomics_train_chunk32.jsonl
ADDED
|
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|
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heldout/metagenomes/data_metagenomics_train_chunk36.done
ADDED
|
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|
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|
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|
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{"shard": "json/pretraining_or_both_phases/metagenomes/data_metagenomics_train_chunk36.jsonl.gz", "rate": 0.001309946368700675, "window": 16384, "seed": 0, "holdout": 0.01, "rule": "per_base", "revision": "84d2a7e690c8bb395d1f4868822dfa031ea561c6", "split": "heldout", "records": 14969, "chars_seen": 106350867, "kept_windows": 24, "kept_bases": 170254}
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heldout/metagenomes/data_metagenomics_train_chunk39.done
ADDED
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|
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| 1 |
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heldout/metagenomes/data_metagenomics_train_chunk39.jsonl
ADDED
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|
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heldout/metagenomes/data_metagenomics_train_chunk40.jsonl
ADDED
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heldout/metagenomes/data_metagenomics_train_chunk5.done
ADDED
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|
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{"shard": "json/pretraining_or_both_phases/metagenomes/data_metagenomics_train_chunk5.jsonl.gz", "rate": 0.001309946368700675, "window": 16384, "seed": 0, "holdout": 0.01, "rule": "per_base", "revision": "84d2a7e690c8bb395d1f4868822dfa031ea561c6", "split": "heldout", "records": 3591, "chars_seen": 106593192, "kept_windows": 11, "kept_bases": 157103}
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heldout/metagenomes/data_metagenomics_train_chunk56.jsonl
ADDED
|
@@ -0,0 +1,54 @@
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| 52 |
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|
heldout/metagenomes/data_metagenomics_train_chunk59.done
ADDED
|
@@ -0,0 +1 @@
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|
|
|
|
|
|
| 1 |
+
{"shard": "json/pretraining_or_both_phases/metagenomes/data_metagenomics_train_chunk59.jsonl.gz", "rate": 0.001309946368700675, "window": 16384, "seed": 0, "holdout": 0.01, "rule": "per_base", "revision": "84d2a7e690c8bb395d1f4868822dfa031ea561c6", "split": "heldout", "records": 37882, "chars_seen": 106150582, "kept_windows": 59, "kept_bases": 165225}
|
heldout/metagenomes/data_metagenomics_train_chunk63.jsonl
ADDED
|
@@ -0,0 +1,48 @@
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heldout/metagenomes/data_metagenomics_train_chunk70.jsonl
ADDED
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heldout/metagenomes/data_metagenomics_train_chunk72.jsonl
ADDED
|
@@ -0,0 +1,84 @@
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heldout/metagenomes/data_metagenomics_train_chunk77.jsonl
ADDED
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@@ -0,0 +1,144 @@
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heldout/metagenomes/data_metagenomics_train_chunk8.done
ADDED
|
@@ -0,0 +1 @@
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|
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| 1 |
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{"shard": "json/pretraining_or_both_phases/metagenomes/data_metagenomics_train_chunk8.jsonl.gz", "rate": 0.001309946368700675, "window": 16384, "seed": 0, "holdout": 0.01, "rule": "per_base", "revision": "84d2a7e690c8bb395d1f4868822dfa031ea561c6", "split": "heldout", "records": 4761, "chars_seen": 105825517, "kept_windows": 21, "kept_bases": 207131}
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heldout/ncrna/data_ncrna_train_chunk1.done
ADDED
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{"shard": "json/pretraining_or_both_phases/ncrna/data_ncrna_train_chunk1.jsonl.gz", "rate": 0.001309946368700675, "window": 16384, "seed": 0, "holdout": 0.01, "rule": "per_base", "revision": "84d2a7e690c8bb395d1f4868822dfa031ea561c6", "split": "heldout", "records": 128783, "chars_seen": 105077347, "kept_windows": 157, "kept_bases": 126141}
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heldout/ncrna/data_ncrna_train_chunk1.jsonl
ADDED
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heldout/ncrna/data_ncrna_train_chunk2.done
ADDED
|
@@ -0,0 +1 @@
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heldout/ncrna/data_ncrna_train_chunk2.jsonl
ADDED
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heldout/ncrna/data_ncrna_train_chunk3.done
ADDED
|
@@ -0,0 +1 @@
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{"shard": "json/pretraining_or_both_phases/ncrna/data_ncrna_train_chunk3.jsonl.gz", "rate": 0.001309946368700675, "window": 16384, "seed": 0, "holdout": 0.01, "rule": "per_base", "revision": "84d2a7e690c8bb395d1f4868822dfa031ea561c6", "split": "heldout", "records": 129805, "chars_seen": 106246548, "kept_windows": 184, "kept_bases": 147997}
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heldout/ncrna/data_ncrna_train_chunk3.jsonl
ADDED
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heldout/ncrna/data_ncrna_train_chunk4.done
ADDED
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heldout/ncrna/data_ncrna_train_chunk4.jsonl
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|
|
|
|
|
|
|
|
|
|
| 1 |
+
{"offset": 0, "len": 1406, "u": 0.000817294, "tag": "", "rec": 137177}
|
| 2 |
+
{"offset": 1406, "len": 229, "u": 0.000612111, "tag": "", "rec": 203148}
|
| 3 |
+
{"offset": 1635, "len": 456, "u": 0.001041174, "tag": "", "rec": 412587}
|
| 4 |
+
{"offset": 2091, "len": 256, "u": 0.000990396, "tag": "", "rec": 433736}
|
| 5 |
+
{"offset": 2347, "len": 460, "u": 0.000821968, "tag": "", "rec": 489101}
|
| 6 |
+
{"offset": 2807, "len": 963, "u": 0.000538924, "tag": "", "rec": 518753}
|
| 7 |
+
{"offset": 3770, "len": 336, "u": 0.000493418, "tag": "", "rec": 560188}
|
| 8 |
+
{"offset": 4106, "len": 3296, "u": 0.001178646, "tag": "", "rec": 566513}
|
| 9 |
+
{"offset": 7402, "len": 253, "u": 0.001014043, "tag": "", "rec": 570587}
|
| 10 |
+
{"offset": 7655, "len": 490, "u": 0.000818753, "tag": "", "rec": 586281}
|
| 11 |
+
{"offset": 8145, "len": 520, "u": 0.000668121, "tag": "", "rec": 699484}
|
| 12 |
+
{"offset": 8665, "len": 81, "u": 0.00125135, "tag": "", "rec": 940836}
|
| 13 |
+
{"offset": 8746, "len": 299, "u": 0.000222097, "tag": "", "rec": 962756}
|
| 14 |
+
{"offset": 9045, "len": 156, "u": 0.000381301, "tag": "", "rec": 992268}
|
| 15 |
+
{"offset": 9201, "len": 368, "u": 0.000855886, "tag": "", "rec": 997026}
|
| 16 |
+
{"offset": 9569, "len": 498, "u": 3.5132e-05, "tag": "", "rec": 1127435}
|
| 17 |
+
{"offset": 10067, "len": 193, "u": 0.000728161, "tag": "", "rec": 1141641}
|
| 18 |
+
{"offset": 10260, "len": 4855, "u": 0.000509521, "tag": "", "rec": 1172348}
|
| 19 |
+
{"offset": 15115, "len": 1483, "u": 0.000393072, "tag": "", "rec": 1175065}
|
heldout/organelle/data_organelle_train_chunk1.done
ADDED
|
@@ -0,0 +1 @@
|
|
|
|
|
|
|
| 1 |
+
{"shard": "json/pretraining_or_both_phases/organelle/data_organelle_train_chunk1.jsonl.gz", "rate": 0.001309946368700675, "window": 16384, "seed": 0, "holdout": 0.01, "rule": "per_base", "revision": "84d2a7e690c8bb395d1f4868822dfa031ea561c6", "split": "heldout", "records": 675, "chars_seen": 62403596, "kept_windows": 6, "kept_bases": 98304}
|
heldout/organelle/data_organelle_train_chunk1.jsonl
ADDED
|
@@ -0,0 +1,6 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
{"offset": 0, "len": 16384, "u": 0.001234764, "tag": "", "rec": 2319}
|
| 2 |
+
{"offset": 16384, "len": 16384, "u": 0.00107382, "tag": "", "rec": 3880}
|
| 3 |
+
{"offset": 32768, "len": 16384, "u": 0.000983968, "tag": "", "rec": 7088}
|
| 4 |
+
{"offset": 49152, "len": 16384, "u": 0.000164015, "tag": "", "rec": 14005}
|
| 5 |
+
{"offset": 65536, "len": 16384, "u": 0.001233064, "tag": "", "rec": 26178}
|
| 6 |
+
{"offset": 81920, "len": 16384, "u": 0.001097059, "tag": "", "rec": 55152}
|
heldout/selected.json
ADDED
|
The diff for this file is too large to render.
See raw diff
|
|
|
plan_train_eukaryotic.json
ADDED
|
@@ -0,0 +1,19 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
{
|
| 2 |
+
"split": "train",
|
| 3 |
+
"target_tokens": 10000000000.0,
|
| 4 |
+
"oversample": 1.15,
|
| 5 |
+
"rate": 0.001309946368700675,
|
| 6 |
+
"window": 16384,
|
| 7 |
+
"seed": 0,
|
| 8 |
+
"holdout": 0.01,
|
| 9 |
+
"shards": 902,
|
| 10 |
+
"compressed_bytes": 2786979481011,
|
| 11 |
+
"tokens_per_byte_assumed": 3.15,
|
| 12 |
+
"rule": "length_weighted",
|
| 13 |
+
"subsets": [
|
| 14 |
+
"ncbi_eukaryotic_genomes"
|
| 15 |
+
],
|
| 16 |
+
"revision": "84d2a7e690c8bb395d1f4868822dfa031ea561c6",
|
| 17 |
+
"source": "arcinstitute/opengenome2",
|
| 18 |
+
"note": "first pass streamed all subsets; only ncbi_eukaryotic_genomes kept"
|
| 19 |
+
}
|
plan_train_rest.json
ADDED
|
@@ -0,0 +1,28 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
{
|
| 2 |
+
"split": "train",
|
| 3 |
+
"target_tokens": 10000000000.0,
|
| 4 |
+
"oversample": 1.0,
|
| 5 |
+
"rate": 0.001309946368700675,
|
| 6 |
+
"window": 16384,
|
| 7 |
+
"seed": 0,
|
| 8 |
+
"holdout": 0.01,
|
| 9 |
+
"shards": 229,
|
| 10 |
+
"compressed_bytes": 694681269852,
|
| 11 |
+
"tokens_per_byte_assumed": 3.316,
|
| 12 |
+
"rule": "per_base",
|
| 13 |
+
"subsets": [
|
| 14 |
+
"metagenomes",
|
| 15 |
+
"eukaryotic_genic_windows",
|
| 16 |
+
"gtdb_v220_imgpr",
|
| 17 |
+
"gtdb_v220_stitched",
|
| 18 |
+
"mrna",
|
| 19 |
+
"imgvr_untagged",
|
| 20 |
+
"mrna_splice_promoter",
|
| 21 |
+
"ncrna",
|
| 22 |
+
"organelle",
|
| 23 |
+
"imgpr",
|
| 24 |
+
"promoters"
|
| 25 |
+
],
|
| 26 |
+
"revision": "84d2a7e690c8bb395d1f4868822dfa031ea561c6",
|
| 27 |
+
"source": "arcinstitute/opengenome2"
|
| 28 |
+
}
|
plan_valid.json
ADDED
|
@@ -0,0 +1,16 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
{
|
| 2 |
+
"split": "valid",
|
| 3 |
+
"target_tokens": 100000000.0,
|
| 4 |
+
"oversample": 1.0,
|
| 5 |
+
"rate": 0.06750641858365167,
|
| 6 |
+
"window": 16384,
|
| 7 |
+
"seed": 0,
|
| 8 |
+
"holdout": 0.0,
|
| 9 |
+
"shards": 52,
|
| 10 |
+
"compressed_bytes": 446725157,
|
| 11 |
+
"tokens_per_byte_assumed": 3.316,
|
| 12 |
+
"rule": "per_base",
|
| 13 |
+
"subsets": "all",
|
| 14 |
+
"revision": "84d2a7e690c8bb395d1f4868822dfa031ea561c6",
|
| 15 |
+
"source": "arcinstitute/opengenome2"
|
| 16 |
+
}
|
scripts/build.sh
ADDED
|
@@ -0,0 +1,41 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
#!/usr/bin/env bash
|
| 2 |
+
# Exact recipe for this dataset. Every stage is deterministic (pinned OpenGenome2 revision, fixed seed and rate) and
|
| 3 |
+
# resumable (finished shards are skipped), so running all stages from scratch reproduces the files bit for bit.
|
| 4 |
+
#
|
| 5 |
+
# Usage (from the dataset root): bash scripts/build.sh [stage ...] stages: euk rest valid finalize stats baseline readme
|
| 6 |
+
# default: all stages in that order. Env: PY (python, default python3), WORKERS (default 14).
|
| 7 |
+
#
|
| 8 |
+
# Two train passes, because the eukaryotic genomes were sampled before a bug in the window rule was found:
|
| 9 |
+
# euk ncbi_eukaryotic_genomes with --rule length_weighted: a window of length L is kept with probability
|
| 10 |
+
# rate * L / 16384. Windows are 16384 bases except the last of each segment, so the realized rate is
|
| 11 |
+
# 0.964 x rate, slightly lower for segment ends (records are ~20 Mb, segments end at tags/contig breaks).
|
| 12 |
+
# rest every other subset with --rule per_base (each window kept with probability `rate`, any length).
|
| 13 |
+
# Both passes use the same seed, rate, window and held-out species (1%), so heldout/ is built by both.
|
| 14 |
+
set -euo pipefail
|
| 15 |
+
cd "$(dirname "$0")/.."
|
| 16 |
+
PY="${PY:-python3}"
|
| 17 |
+
W="${WORKERS:-14}"
|
| 18 |
+
RATE=0.001309946368700675
|
| 19 |
+
COMMON=(--seed 0 --window 16384 --holdout 0.01 --rate "$RATE" --workers "$W" --out .)
|
| 20 |
+
REST=(metagenomes eukaryotic_genic_windows gtdb_v220_imgpr gtdb_v220_stitched mrna imgvr_untagged
|
| 21 |
+
mrna_splice_promoter ncrna organelle imgpr promoters)
|
| 22 |
+
STAGES=("$@")
|
| 23 |
+
[ ${#STAGES[@]} -gt 0 ] || STAGES=(euk rest valid finalize stats baseline readme)
|
| 24 |
+
|
| 25 |
+
for stage in "${STAGES[@]}"; do
|
| 26 |
+
echo "== $stage $(date)"
|
| 27 |
+
case "$stage" in
|
| 28 |
+
euk) "$PY" scripts/og2subset.py sample --split train --subsets ncbi_eukaryotic_genomes --rule length_weighted \
|
| 29 |
+
"${COMMON[@]}" --plan plan_train_eukaryotic.json ;;
|
| 30 |
+
rest) "$PY" scripts/og2subset.py sample --split train --subsets "${REST[@]}" --rule per_base \
|
| 31 |
+
"${COMMON[@]}" --plan plan_train_rest.json ;;
|
| 32 |
+
valid) "$PY" scripts/og2subset.py sample --split valid --target-tokens 100e6 --rule per_base --seed 0 \
|
| 33 |
+
--window 16384 --workers "$W" --out . --plan plan_valid.json ;;
|
| 34 |
+
finalize) for s in train heldout valid; do "$PY" scripts/og2subset.py finalize --split "$s" --out .; done ;;
|
| 35 |
+
stats) "$PY" scripts/og2stats.py --root . --workers "$W" ;;
|
| 36 |
+
baseline) "$PY" scripts/og2baseline.py --root . --workers "$W" ;;
|
| 37 |
+
readme) if [ -f scripts/make_readme.py ]; then "$PY" scripts/make_readme.py --root .; else echo "skip: no make_readme.py"; fi ;;
|
| 38 |
+
*) echo "unknown stage $stage"; exit 1 ;;
|
| 39 |
+
esac
|
| 40 |
+
done
|
| 41 |
+
echo "== done $(date)"
|
scripts/check_rates.py
ADDED
|
@@ -0,0 +1,37 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
"""Check uniformity: realized inclusion rate (kept bases / characters streamed) per subset and split, vs the target.
|
| 2 |
+
|
| 3 |
+
python check_rates.py [--root .] [--split train]
|
| 4 |
+
|
| 5 |
+
A per-base uniform sample keeps rate * (sequence characters) per subset; characters streamed also count tag and
|
| 6 |
+
separator characters, so a subset with many tags or separators shows a slightly lower realized rate.
|
| 7 |
+
"""
|
| 8 |
+
|
| 9 |
+
from __future__ import annotations
|
| 10 |
+
|
| 11 |
+
import argparse
|
| 12 |
+
import json
|
| 13 |
+
from collections import defaultdict
|
| 14 |
+
from pathlib import Path
|
| 15 |
+
|
| 16 |
+
|
| 17 |
+
def main() -> None:
|
| 18 |
+
ap = argparse.ArgumentParser()
|
| 19 |
+
ap.add_argument("--root", type=Path, default=Path("."))
|
| 20 |
+
ap.add_argument("--split", default="train")
|
| 21 |
+
a = ap.parse_args()
|
| 22 |
+
agg = defaultdict(lambda: [0, 0, 0, 0.0])
|
| 23 |
+
for done in sorted((a.root / a.split).glob("*/*.done")):
|
| 24 |
+
st = json.loads(done.read_text())
|
| 25 |
+
g = agg[done.parent.name]
|
| 26 |
+
g[0] += st["kept_bases"]
|
| 27 |
+
g[1] += st["chars_seen"]
|
| 28 |
+
g[2] += st["records"]
|
| 29 |
+
g[3] = st["rate"]
|
| 30 |
+
print(f"{'subset':<28} {'records':>12} {'chars/record':>13} {'kept/chars':>11} {'ratio to rate':>14}")
|
| 31 |
+
for k, (kept, chars, recs, rate) in sorted(agg.items(), key=lambda kv: -kv[1][1]):
|
| 32 |
+
r = kept / max(chars, 1)
|
| 33 |
+
print(f"{k:<28} {recs:12d} {chars / max(recs, 1):13.0f} {r:11.3e} {r / rate:14.3f}")
|
| 34 |
+
|
| 35 |
+
|
| 36 |
+
if __name__ == "__main__":
|
| 37 |
+
main()
|
scripts/og2baseline.py
ADDED
|
@@ -0,0 +1,120 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
"""k-mer (Markov) baselines: bits per base of order-0..7 models fitted on train, scored out of sample.
|
| 2 |
+
|
| 3 |
+
python og2baseline.py [--root .] [--fit train] [--eval heldout valid] [--alpha 0.5] [--workers 8]
|
| 4 |
+
|
| 5 |
+
Fit: the train split's 8-mer counts (stats/train_kmer8.npz from og2stats.py; case folded, k-mers with N or crossing a
|
| 6 |
+
window boundary excluded) give, for each order k, P(b | previous k bases) = (c(ctx, b) + alpha) / (c(ctx) + 4 alpha).
|
| 7 |
+
Score: on each evaluation split, every position i of every window whose target and 7 previous bases are A/C/G/T (the
|
| 8 |
+
same positions for every order, so orders are comparable; these are exactly the targets kept by the loss mask, minus
|
| 9 |
+
the first 7 of each window). Reports bits/base overall and per subset, plus the best order, and the in-sample value on
|
| 10 |
+
train for reference (lower than out of sample: the model has seen those counts).
|
| 11 |
+
Writes stats/baseline.json. A trained model's masked bits/base on heldout should be compared with the best order.
|
| 12 |
+
"""
|
| 13 |
+
|
| 14 |
+
from __future__ import annotations
|
| 15 |
+
|
| 16 |
+
import argparse
|
| 17 |
+
import json
|
| 18 |
+
from collections import defaultdict
|
| 19 |
+
from concurrent.futures import ProcessPoolExecutor
|
| 20 |
+
from pathlib import Path
|
| 21 |
+
|
| 22 |
+
import numpy as np
|
| 23 |
+
|
| 24 |
+
K = 8
|
| 25 |
+
ORDERS = list(range(K))
|
| 26 |
+
|
| 27 |
+
|
| 28 |
+
def tables(k8: np.ndarray, alpha: float) -> dict[int, np.ndarray]:
|
| 29 |
+
"""log2 P(b | ctx) for each order: [4**k, 4] arrays."""
|
| 30 |
+
t = k8.reshape([4] * K).astype(np.float64)
|
| 31 |
+
out = {}
|
| 32 |
+
for k in ORDERS:
|
| 33 |
+
c = t.sum(axis=tuple(range(k + 1, K))) if k + 1 < K else t # (k+1)-mer counts
|
| 34 |
+
c = c.reshape(4**k, 4)
|
| 35 |
+
out[k] = np.log2((c + alpha) / (c.sum(1, keepdims=True) + 4 * alpha))
|
| 36 |
+
return out
|
| 37 |
+
|
| 38 |
+
|
| 39 |
+
_T: dict[int, np.ndarray] = {}
|
| 40 |
+
|
| 41 |
+
|
| 42 |
+
def _init(tabs: dict[int, np.ndarray]) -> None:
|
| 43 |
+
_T.update(tabs)
|
| 44 |
+
|
| 45 |
+
|
| 46 |
+
def score_shard(npy: str, ids: list[int] | None) -> dict:
|
| 47 |
+
"""Summed -log2 P per order and position count for one shard."""
|
| 48 |
+
base = Path(npy).with_suffix("")
|
| 49 |
+
arr = np.load(npy, mmap_mode="r")
|
| 50 |
+
rows = [json.loads(line) for line in base.with_suffix(".jsonl").read_text().splitlines()]
|
| 51 |
+
if ids is not None:
|
| 52 |
+
rows = [rows[i] for i in ids]
|
| 53 |
+
bits = np.zeros(K)
|
| 54 |
+
n = 0
|
| 55 |
+
for r in rows:
|
| 56 |
+
b = (np.asarray(arr[r["offset"]: r["offset"] + r["len"]]) & 7).astype(np.int64)
|
| 57 |
+
L = len(b)
|
| 58 |
+
if L <= K - 1:
|
| 59 |
+
continue
|
| 60 |
+
bad = np.concatenate([[0], np.cumsum(b >= 4)])
|
| 61 |
+
pos = np.arange(K - 1, L) # target positions with 7 previous bases
|
| 62 |
+
ok = (bad[pos + 1] - bad[pos - (K - 1)]) == 0 # context + target all A/C/G/T
|
| 63 |
+
pos = pos[ok]
|
| 64 |
+
if len(pos) == 0:
|
| 65 |
+
continue
|
| 66 |
+
tgt = b[pos]
|
| 67 |
+
ctx = np.zeros(len(pos), dtype=np.int64)
|
| 68 |
+
for k in ORDERS: # ctx = code of the k bases before the target (grows one base per order)
|
| 69 |
+
if k > 0:
|
| 70 |
+
ctx = ctx + b[pos - k] * 4 ** (k - 1)
|
| 71 |
+
bits[k] -= _T[k][ctx, tgt].sum()
|
| 72 |
+
n += len(pos)
|
| 73 |
+
return {"subset": base.parent.name, "bits": bits.tolist(), "n": n}
|
| 74 |
+
|
| 75 |
+
|
| 76 |
+
def evaluate(root: Path, split: str, tabs: dict, workers: int) -> dict:
|
| 77 |
+
sel_path = root / split / "selected.json"
|
| 78 |
+
sel = json.loads(sel_path.read_text())["shards"] if sel_path.exists() else None
|
| 79 |
+
jobs = [(str(p), None if sel is None else sel.get(str(p.with_suffix("").relative_to(root)), []))
|
| 80 |
+
for p in sorted((root / split).glob("*/*.npy"))]
|
| 81 |
+
agg: dict[str, list] = defaultdict(lambda: [np.zeros(K), 0])
|
| 82 |
+
with ProcessPoolExecutor(workers, initializer=_init, initargs=(tabs,)) as ex:
|
| 83 |
+
for res in ex.map(score_shard, *zip(*jobs), chunksize=4):
|
| 84 |
+
g = agg[res["subset"]]
|
| 85 |
+
g[0] += np.array(res["bits"])
|
| 86 |
+
g[1] += res["n"]
|
| 87 |
+
tot_bits = sum((g[0] for g in agg.values()), np.zeros(K))
|
| 88 |
+
tot_n = sum(g[1] for g in agg.values())
|
| 89 |
+
|
| 90 |
+
def row(bits, n):
|
| 91 |
+
bpb = {str(k): round(float(bits[k] / max(n, 1)), 5) for k in ORDERS}
|
| 92 |
+
best = min(ORDERS, key=lambda k: bpb[str(k)])
|
| 93 |
+
return {"positions": int(n), "bits_per_base": bpb, "best_order": best, "best": bpb[str(best)]}
|
| 94 |
+
|
| 95 |
+
return {"overall": row(tot_bits, tot_n),
|
| 96 |
+
"subsets": {k: row(g[0], g[1]) for k, g in sorted(agg.items(), key=lambda kv: -kv[1][1])}}
|
| 97 |
+
|
| 98 |
+
|
| 99 |
+
def main(argv: list[str] | None = None) -> None:
|
| 100 |
+
ap = argparse.ArgumentParser(description=__doc__, formatter_class=argparse.RawDescriptionHelpFormatter)
|
| 101 |
+
ap.add_argument("--root", type=Path, default=Path("."))
|
| 102 |
+
ap.add_argument("--fit", default="train")
|
| 103 |
+
ap.add_argument("--eval", nargs="+", default=["heldout", "valid", "train"])
|
| 104 |
+
ap.add_argument("--alpha", type=float, default=0.5)
|
| 105 |
+
ap.add_argument("--workers", type=int, default=8)
|
| 106 |
+
a = ap.parse_args(argv)
|
| 107 |
+
k8 = np.load(a.root / "stats" / f"{a.fit}_kmer8.npz")["overall"]
|
| 108 |
+
tabs = tables(k8, a.alpha)
|
| 109 |
+
out = {"fit": a.fit, "alpha": a.alpha, "orders": ORDERS, "splits": {}}
|
| 110 |
+
for split in a.eval:
|
| 111 |
+
if (a.root / split).exists():
|
| 112 |
+
out["splits"][split] = r = evaluate(a.root, split, tabs, a.workers)
|
| 113 |
+
o = r["overall"]
|
| 114 |
+
print(f"{split}: " + " ".join(f"k{k}={v:.4f}" for k, v in o["bits_per_base"].items())
|
| 115 |
+
+ f" best k{o['best_order']} {o['best']:.4f} ({o['positions']:,} positions)")
|
| 116 |
+
(a.root / "stats" / "baseline.json").write_text(json.dumps(out, indent=1))
|
| 117 |
+
|
| 118 |
+
|
| 119 |
+
if __name__ == "__main__":
|
| 120 |
+
main()
|
scripts/og2dataloader.py
ADDED
|
@@ -0,0 +1,131 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
"""PyTorch data loading for the subset: next-base prediction samples with an optional loss mask for non-ACGT targets.
|
| 2 |
+
|
| 3 |
+
from og2dataloader import RandomCrops, EvalTiles, make_loader
|
| 4 |
+
train = RandomCrops(root, "train", ctx=4096, seed=0, rc_prob=0.5, mask_non_acgt=True)
|
| 5 |
+
loader = make_loader(train, batch_size=16, num_workers=4) # infinite stream of batches
|
| 6 |
+
for batch in loader:
|
| 7 |
+
batch["input_ids"] # [B, ctx] int64, A=0 C=1 G=2 T=3 N/other=4 (case dropped)
|
| 8 |
+
batch["labels"] # [B, ctx] int64, the next base; -100 where the target is not A/C/G/T (if mask_non_acgt)
|
| 9 |
+
batch["loss_mask"] # [B, ctx] bool, True where the loss counts
|
| 10 |
+
batch["lowercase"] # [B, ctx] bool, the target is soft-masked (repeat); for weighting or per-class metrics
|
| 11 |
+
heldout = EvalTiles(root, "heldout", ctx=4096) # every window tiled once, deterministic order
|
| 12 |
+
loss = F.cross_entropy(logits.flatten(0, 1), batch["labels"].flatten(), ignore_index=-100)
|
| 13 |
+
|
| 14 |
+
A sample is ctx + 1 consecutive bases of one window (windows never cross record, contig or tag boundaries):
|
| 15 |
+
input = bases[:-1], labels = bases[1:]. With mask_non_acgt=False every target counts and N is a fifth class.
|
| 16 |
+
Inputs keep N (id 4) either way, so a model sees gaps. RandomCrops draws windows with probability proportional to
|
| 17 |
+
their number of possible crops (so every base is equally likely to be trained on), reproducibly per (seed, worker).
|
| 18 |
+
EvalTiles cuts every window into consecutive non-overlapping ctx + 1 tiles (the remainder shorter, right-padded
|
| 19 |
+
with labels -100); windows shorter than 2 bases are skipped.
|
| 20 |
+
Filters: subset="metagenomes", taxon=r"P__CHORDATA" (regex on the window's taxonomy tag), as in og2load.Windows.
|
| 21 |
+
"""
|
| 22 |
+
|
| 23 |
+
from __future__ import annotations
|
| 24 |
+
|
| 25 |
+
import numpy as np
|
| 26 |
+
from og2load import Windows
|
| 27 |
+
|
| 28 |
+
try:
|
| 29 |
+
import torch
|
| 30 |
+
from torch.utils.data import DataLoader, Dataset, IterableDataset, get_worker_info
|
| 31 |
+
except ImportError as e: # pragma: no cover - torch is optional for the rest of the scripts
|
| 32 |
+
raise ImportError("og2dataloader needs PyTorch: pip install torch") from e
|
| 33 |
+
|
| 34 |
+
IGNORE = -100
|
| 35 |
+
COMP = np.array([3, 2, 1, 0, 4], dtype=np.int64) # A<->T, C<->G, N->N
|
| 36 |
+
|
| 37 |
+
|
| 38 |
+
def to_sample(codes: np.ndarray, mask_non_acgt: bool, rc: bool = False, pad_to: int | None = None) -> dict:
|
| 39 |
+
"""uint8 codes of ctx + 1 bases -> one sample (tensors)."""
|
| 40 |
+
base = (codes & 7).astype(np.int64)
|
| 41 |
+
lower = (codes & 0x80) != 0
|
| 42 |
+
if rc:
|
| 43 |
+
base, lower = COMP[base[::-1]], lower[::-1]
|
| 44 |
+
x, y, low = base[:-1], base[1:].copy(), lower[1:].copy()
|
| 45 |
+
mask = y < 4 if mask_non_acgt else np.ones_like(y, dtype=bool)
|
| 46 |
+
y[~mask] = IGNORE
|
| 47 |
+
if pad_to is not None and len(x) < pad_to:
|
| 48 |
+
n = pad_to - len(x)
|
| 49 |
+
x = np.concatenate([x, np.full(n, 4, dtype=np.int64)])
|
| 50 |
+
y = np.concatenate([y, np.full(n, IGNORE, dtype=np.int64)])
|
| 51 |
+
mask = np.concatenate([mask, np.zeros(n, dtype=bool)])
|
| 52 |
+
low = np.concatenate([low, np.zeros(n, dtype=bool)])
|
| 53 |
+
return {"input_ids": torch.from_numpy(np.ascontiguousarray(x)), "labels": torch.from_numpy(np.ascontiguousarray(y)),
|
| 54 |
+
"loss_mask": torch.from_numpy(np.ascontiguousarray(mask)),
|
| 55 |
+
"lowercase": torch.from_numpy(np.ascontiguousarray(low))}
|
| 56 |
+
|
| 57 |
+
|
| 58 |
+
class RandomCrops(IterableDataset):
|
| 59 |
+
"""Infinite stream of random ctx + 1 crops, length-weighted over windows; reproducible per (seed, worker)."""
|
| 60 |
+
|
| 61 |
+
def __init__(self, root, split: str = "train", ctx: int = 4096, seed: int = 0, rc_prob: float = 0.0,
|
| 62 |
+
mask_non_acgt: bool = True, subset: str | None = None, taxon: str | None = None) -> None:
|
| 63 |
+
self.windows = Windows(root, split, subset, taxon)
|
| 64 |
+
self.ctx, self.seed, self.rc_prob, self.mask = ctx, seed, rc_prob, mask_non_acgt
|
| 65 |
+
usable = np.maximum(self.windows.lengths - ctx, 0) # crops of ctx + 1 bases per window
|
| 66 |
+
if usable.sum() == 0:
|
| 67 |
+
raise ValueError(f"no window in {split} has {ctx + 1} bases")
|
| 68 |
+
self.p = usable / usable.sum()
|
| 69 |
+
self.usable = usable
|
| 70 |
+
|
| 71 |
+
def __iter__(self):
|
| 72 |
+
info = get_worker_info()
|
| 73 |
+
rng = np.random.default_rng([self.seed, info.id if info else 0])
|
| 74 |
+
while True:
|
| 75 |
+
i = int(rng.choice(len(self.p), p=self.p))
|
| 76 |
+
arr, m = self.windows.items[i]
|
| 77 |
+
s = m["offset"] + int(rng.integers(0, self.usable[i]))
|
| 78 |
+
codes = np.asarray(arr[s: s + self.ctx + 1])
|
| 79 |
+
yield to_sample(codes, self.mask, rc=bool(rng.random() < self.rc_prob))
|
| 80 |
+
|
| 81 |
+
|
| 82 |
+
class EvalTiles(Dataset):
|
| 83 |
+
"""Every window cut into consecutive ctx + 1 tiles (deterministic); for bits/base on heldout or valid."""
|
| 84 |
+
|
| 85 |
+
def __init__(self, root, split: str = "heldout", ctx: int = 4096, mask_non_acgt: bool = True,
|
| 86 |
+
subset: str | None = None, taxon: str | None = None) -> None:
|
| 87 |
+
self.windows = Windows(root, split, subset, taxon)
|
| 88 |
+
self.ctx, self.mask = ctx, mask_non_acgt
|
| 89 |
+
self.tiles: list[tuple[int, int, int]] = [] # (window index, start, length incl. the extra base)
|
| 90 |
+
for i, (_, m) in enumerate(self.windows.items):
|
| 91 |
+
for s in range(0, m["len"] - 1, ctx):
|
| 92 |
+
n = min(ctx + 1, m["len"] - s)
|
| 93 |
+
if n >= 2:
|
| 94 |
+
self.tiles.append((i, s, n))
|
| 95 |
+
|
| 96 |
+
def __len__(self) -> int:
|
| 97 |
+
return len(self.tiles)
|
| 98 |
+
|
| 99 |
+
def __getitem__(self, k: int) -> dict:
|
| 100 |
+
i, s, n = self.tiles[k]
|
| 101 |
+
arr, m = self.windows.items[i]
|
| 102 |
+
codes = np.asarray(arr[m["offset"] + s: m["offset"] + s + n])
|
| 103 |
+
out = to_sample(codes, self.mask, pad_to=self.ctx)
|
| 104 |
+
out["subset"] = m["subset"]
|
| 105 |
+
return out
|
| 106 |
+
|
| 107 |
+
|
| 108 |
+
def collate(samples: list[dict]) -> dict:
|
| 109 |
+
out = {k: torch.stack([s[k] for s in samples]) for k in ("input_ids", "labels", "loss_mask", "lowercase")}
|
| 110 |
+
if "subset" in samples[0]:
|
| 111 |
+
out["subset"] = [s["subset"] for s in samples]
|
| 112 |
+
return out
|
| 113 |
+
|
| 114 |
+
|
| 115 |
+
def make_loader(ds, batch_size: int, num_workers: int = 0) -> DataLoader:
|
| 116 |
+
return DataLoader(ds, batch_size=batch_size, num_workers=num_workers, collate_fn=collate,
|
| 117 |
+
persistent_workers=num_workers > 0)
|
| 118 |
+
|
| 119 |
+
|
| 120 |
+
if __name__ == "__main__":
|
| 121 |
+
import argparse
|
| 122 |
+
|
| 123 |
+
ap = argparse.ArgumentParser(description=__doc__, formatter_class=argparse.RawDescriptionHelpFormatter)
|
| 124 |
+
ap.add_argument("--root", default=".")
|
| 125 |
+
ap.add_argument("--split", default="train")
|
| 126 |
+
ap.add_argument("--ctx", type=int, default=4096)
|
| 127 |
+
ap.add_argument("--batch", type=int, default=4)
|
| 128 |
+
a = ap.parse_args()
|
| 129 |
+
b = next(iter(make_loader(RandomCrops(a.root, a.split, a.ctx, rc_prob=0.5), a.batch)))
|
| 130 |
+
print({k: (tuple(v.shape), v.dtype) for k, v in b.items()})
|
| 131 |
+
print(f"masked targets: {(~b['loss_mask']).float().mean():.5f}, soft-masked targets: {b['lowercase'].float().mean():.3f}")
|
scripts/og2load.py
ADDED
|
@@ -0,0 +1,87 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
|
|
|
|
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|
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
|
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|
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|
|
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|
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|
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|
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|
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
|
|
|
|
|
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|
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|
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|
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|
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|
|
| 1 |
+
"""Read the subset: iterate windows (optionally by subset or taxon) or draw random training crops.
|
| 2 |
+
|
| 3 |
+
python og2load.py [--root .] [--split train] [--subset ncbi_eukaryotic_genomes] [--taxon "S__HOMO SAPIENS"] [--n 3]
|
| 4 |
+
|
| 5 |
+
Library use:
|
| 6 |
+
from og2load import Windows
|
| 7 |
+
ds = Windows(root, "train", taxon=r"P__CHORDATA") # memory-maps every shard's .npy
|
| 8 |
+
for codes, meta in ds: # uint8 codes of one window + its index row and subset
|
| 9 |
+
...
|
| 10 |
+
x = ds.random_crops(batch=8, length=4096, rng=np.random.default_rng(0)) # [8, 4096] uint8, length-weighted
|
| 11 |
+
|
| 12 |
+
Codes: low 3 bits A=0 C=1 G=2 T=3 N=4, bit 7 (0x80) = lowercase (soft-masked repeat); `codes & 7` drops case.
|
| 13 |
+
"""
|
| 14 |
+
|
| 15 |
+
from __future__ import annotations
|
| 16 |
+
|
| 17 |
+
import argparse
|
| 18 |
+
import json
|
| 19 |
+
import re
|
| 20 |
+
from collections.abc import Iterator
|
| 21 |
+
from pathlib import Path
|
| 22 |
+
|
| 23 |
+
import numpy as np
|
| 24 |
+
|
| 25 |
+
|
| 26 |
+
class Windows:
|
| 27 |
+
def __init__(self, root: Path | str, split: str, subset: str | None = None, taxon: str | None = None) -> None:
|
| 28 |
+
root = Path(root)
|
| 29 |
+
sel_path = root / split / "selected.json"
|
| 30 |
+
sel = json.loads(sel_path.read_text())["shards"] if sel_path.exists() else None
|
| 31 |
+
pat = re.compile(taxon) if taxon else None
|
| 32 |
+
self.items: list[tuple[np.ndarray, dict]] = []
|
| 33 |
+
for npy in sorted((root / split).glob("*/*.npy")):
|
| 34 |
+
sub = npy.parent.name
|
| 35 |
+
if subset and sub != subset:
|
| 36 |
+
continue
|
| 37 |
+
key = str(npy.with_suffix("").relative_to(root))
|
| 38 |
+
rows = [json.loads(line) for line in npy.with_suffix(".jsonl").read_text().splitlines()]
|
| 39 |
+
keep = range(len(rows)) if sel is None else sel.get(key, [])
|
| 40 |
+
arr = np.load(npy, mmap_mode="r")
|
| 41 |
+
for i in keep:
|
| 42 |
+
r = rows[i]
|
| 43 |
+
if pat is None or pat.search(r["tag"]):
|
| 44 |
+
self.items.append((arr, {**r, "subset": sub, "shard": key}))
|
| 45 |
+
self.lengths = np.array([m["len"] for _, m in self.items], dtype=np.int64)
|
| 46 |
+
|
| 47 |
+
def __len__(self) -> int:
|
| 48 |
+
return len(self.items)
|
| 49 |
+
|
| 50 |
+
def __iter__(self) -> Iterator[tuple[np.ndarray, dict]]:
|
| 51 |
+
for arr, m in self.items:
|
| 52 |
+
yield np.asarray(arr[m["offset"]: m["offset"] + m["len"]]), m
|
| 53 |
+
|
| 54 |
+
def random_crops(self, batch: int, length: int, rng: np.random.Generator) -> np.ndarray:
|
| 55 |
+
"""Crops of `length` bases from windows chosen with probability proportional to their usable length."""
|
| 56 |
+
usable = np.maximum(self.lengths - length + 1, 0)
|
| 57 |
+
if usable.sum() == 0:
|
| 58 |
+
raise ValueError(f"no window has {length} bases")
|
| 59 |
+
idx = rng.choice(len(self.items), size=batch, p=usable / usable.sum())
|
| 60 |
+
out = np.empty((batch, length), dtype=np.uint8)
|
| 61 |
+
for j, i in enumerate(idx):
|
| 62 |
+
arr, m = self.items[i]
|
| 63 |
+
s = m["offset"] + int(rng.integers(0, usable[i]))
|
| 64 |
+
out[j] = arr[s: s + length]
|
| 65 |
+
return out
|
| 66 |
+
|
| 67 |
+
|
| 68 |
+
def main() -> None:
|
| 69 |
+
ap = argparse.ArgumentParser()
|
| 70 |
+
ap.add_argument("--root", default=".")
|
| 71 |
+
ap.add_argument("--split", default="train")
|
| 72 |
+
ap.add_argument("--subset", default=None)
|
| 73 |
+
ap.add_argument("--taxon", default=None, help="regex on the window's taxonomy tag, e.g. 'S__HOMO SAPIENS'")
|
| 74 |
+
ap.add_argument("--n", type=int, default=3)
|
| 75 |
+
a = ap.parse_args()
|
| 76 |
+
ds = Windows(a.root, a.split, a.subset, a.taxon)
|
| 77 |
+
print(f"{len(ds)} windows, {int(ds.lengths.sum()):,} bases")
|
| 78 |
+
up, low = np.frombuffer(b"ACGTN", dtype=np.uint8), np.frombuffer(b"acgtn", dtype=np.uint8)
|
| 79 |
+
for k, (codes, m) in enumerate(ds):
|
| 80 |
+
if k >= a.n:
|
| 81 |
+
break
|
| 82 |
+
s = np.where(codes & 0x80, low[codes & 7], up[codes & 7]).astype(np.uint8).tobytes().decode()
|
| 83 |
+
print(f"{m['subset']} {m['len']} bp {m['tag'][:90]}\n {s[:100]}...")
|
| 84 |
+
|
| 85 |
+
|
| 86 |
+
if __name__ == "__main__":
|
| 87 |
+
main()
|
scripts/og2stats.py
ADDED
|
@@ -0,0 +1,150 @@
|
|
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|
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|
|
|
|
|
|
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|
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|
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|
|
|
|
|
|
|
|
|
|
|
|
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
"""Gross statistics of the subset: base frequencies, GC, soft-masking, CpG, k-mer spectra, Markov entropies.
|
| 2 |
+
|
| 3 |
+
python og2stats.py [--root .] [--splits train heldout valid] [--workers 8]
|
| 4 |
+
|
| 5 |
+
For every split, per subset (directory), per domain (D__ of the window's tag; '(untagged)' otherwise) and overall:
|
| 6 |
+
tokens, windows; counts of A, C, G, T, N; GC fraction (of ACGT); soft-masked (lowercase) fraction;
|
| 7 |
+
CpG observed/expected = f(CG) / (f(C) f(G)) from dinucleotides.
|
| 8 |
+
Overall and per subset, from 8-mer counts (case folded; k-mers containing N or crossing a window boundary skipped):
|
| 9 |
+
k-mer counts for k = 1..8 (by marginalising the 8-mer counts over trailing positions), the entropy H_k of each
|
| 10 |
+
k-mer distribution and the Markov conditional entropies h_k = H_{k+1} - H_k (bits per base given the previous k
|
| 11 |
+
bases: the in-sample bits/base of an order-k model), distinct 8-mers, the 20 most frequent 8-mers, and
|
| 12 |
+
Chargaff's second rule (mean |f(w) - f(revcomp w)| / (f(w) + f(revcomp w)) over 4-mers).
|
| 13 |
+
Windows are those listed in <split>/selected.json when present (`og2subset.py finalize`), else all.
|
| 14 |
+
Writes <root>/stats/<split>.json and <root>/stats/<split>_kmer8.npz (8-mer counts, overall and per subset).
|
| 15 |
+
"""
|
| 16 |
+
|
| 17 |
+
from __future__ import annotations
|
| 18 |
+
|
| 19 |
+
import argparse
|
| 20 |
+
import json
|
| 21 |
+
import math
|
| 22 |
+
import re
|
| 23 |
+
from collections import defaultdict
|
| 24 |
+
from concurrent.futures import ProcessPoolExecutor
|
| 25 |
+
from pathlib import Path
|
| 26 |
+
|
| 27 |
+
import numpy as np
|
| 28 |
+
|
| 29 |
+
K = 8
|
| 30 |
+
BASES = "ACGT"
|
| 31 |
+
|
| 32 |
+
|
| 33 |
+
def taxon(tag: str, rank: str) -> str:
|
| 34 |
+
m = re.search(rf"{rank}__([^;|]*)", tag)
|
| 35 |
+
return m.group(1) if m else "(untagged)"
|
| 36 |
+
|
| 37 |
+
|
| 38 |
+
def shard_stats(npy: str, ids: list[int] | None) -> dict:
|
| 39 |
+
"""Counts for one shard: per domain base/lower/CpG counts and the shard's 8-mer counts."""
|
| 40 |
+
base = Path(npy).with_suffix("")
|
| 41 |
+
arr = np.load(npy)
|
| 42 |
+
rows = [json.loads(line) for line in base.with_suffix(".jsonl").read_text().splitlines()]
|
| 43 |
+
if ids is not None:
|
| 44 |
+
rows = [rows[i] for i in ids]
|
| 45 |
+
kmer = np.zeros(4**K, dtype=np.int64)
|
| 46 |
+
dom: dict[str, np.ndarray] = defaultdict(lambda: np.zeros(8, dtype=np.int64)) # A C G T N lower CG windows
|
| 47 |
+
for r in rows:
|
| 48 |
+
w = arr[r["offset"]: r["offset"] + r["len"]]
|
| 49 |
+
b = (w & 7).astype(np.int64)
|
| 50 |
+
d = dom[taxon(r["tag"], "D")]
|
| 51 |
+
d[:5] += np.bincount(b, minlength=5)[:5]
|
| 52 |
+
d[5] += int(np.count_nonzero(w & 0x80))
|
| 53 |
+
if len(b) > 1:
|
| 54 |
+
d[6] += int(np.count_nonzero((b[:-1] == 1) & (b[1:] == 2)))
|
| 55 |
+
d[7] += 1
|
| 56 |
+
n = len(b) - K + 1
|
| 57 |
+
if n <= 0:
|
| 58 |
+
continue
|
| 59 |
+
bad = np.concatenate([[0], np.cumsum(b >= 4)])
|
| 60 |
+
ok = (bad[K:] - bad[:-K]) == 0 # no N in positions i..i+K-1
|
| 61 |
+
idx = np.zeros(n, dtype=np.int64)
|
| 62 |
+
for j in range(K):
|
| 63 |
+
idx = idx * 4 + np.minimum(b[j:j + n], 3)
|
| 64 |
+
kmer += np.bincount(idx[ok], minlength=4**K)
|
| 65 |
+
return {"subset": base.parent.name, "domains": {k: v.tolist() for k, v in dom.items()}, "kmer": kmer}
|
| 66 |
+
|
| 67 |
+
|
| 68 |
+
def entropy(counts: np.ndarray) -> float:
|
| 69 |
+
p = counts[counts > 0] / counts.sum()
|
| 70 |
+
return float(-(p * np.log2(p)).sum())
|
| 71 |
+
|
| 72 |
+
|
| 73 |
+
def kmer_summary(k8: np.ndarray) -> dict:
|
| 74 |
+
if k8.sum() == 0:
|
| 75 |
+
return {}
|
| 76 |
+
t = k8.reshape([4] * K)
|
| 77 |
+
H = {}
|
| 78 |
+
for k in range(1, K + 1):
|
| 79 |
+
ck = t.sum(axis=tuple(range(k, K))) if k < K else t
|
| 80 |
+
H[k] = entropy(ck.ravel())
|
| 81 |
+
h = {0: H[1], **{k: H[k + 1] - H[k] for k in range(1, K)}}
|
| 82 |
+
c4 = t.sum(axis=tuple(range(4, K))).ravel().astype(float)
|
| 83 |
+
rc = np.array([int("".join(str(3 - int(c)) for c in np.base_repr(i, 4).zfill(4)[::-1]), 4) for i in range(256)])
|
| 84 |
+
ch2 = float(np.mean(np.abs(c4 - c4[rc]) / np.maximum(c4 + c4[rc], 1)))
|
| 85 |
+
top = np.argsort(k8)[::-1][:20]
|
| 86 |
+
word = lambda i: "".join(BASES[int(c)] for c in np.base_repr(int(i), 4).zfill(K))
|
| 87 |
+
return {"kmers_counted": int(k8.sum()), "distinct_8mers": int((k8 > 0).sum()),
|
| 88 |
+
"entropy_bits": {str(k): round(v, 5) for k, v in H.items()},
|
| 89 |
+
"markov_bits_per_base": {str(k): round(v, 5) for k, v in h.items()},
|
| 90 |
+
"chargaff2_4mer_asymmetry": round(ch2, 5),
|
| 91 |
+
"top_8mers": [[word(i), round(float(k8[i] / k8.sum()), 6)] for i in top]}
|
| 92 |
+
|
| 93 |
+
|
| 94 |
+
def base_summary(c: np.ndarray) -> dict:
|
| 95 |
+
a, cc, g, t, n, low, cg, win = (int(x) for x in c)
|
| 96 |
+
acgt = a + cc + g + t
|
| 97 |
+
tot = acgt + n
|
| 98 |
+
fc, fg = cc / max(acgt, 1), g / max(acgt, 1)
|
| 99 |
+
return {"tokens": tot, "windows": win,
|
| 100 |
+
"freq": {"A": a / max(tot, 1), "C": cc / max(tot, 1), "G": g / max(tot, 1), "T": t / max(tot, 1),
|
| 101 |
+
"N": n / max(tot, 1)},
|
| 102 |
+
"gc": (cc + g) / max(acgt, 1), "soft_masked": low / max(tot, 1),
|
| 103 |
+
"cpg_obs_exp": (cg / max(acgt - win, 1)) / (fc * fg) if fc * fg > 0 else math.nan}
|
| 104 |
+
|
| 105 |
+
|
| 106 |
+
def split_stats(root: Path, split: str, workers: int) -> dict:
|
| 107 |
+
sel_path = root / split / "selected.json"
|
| 108 |
+
sel = json.loads(sel_path.read_text())["shards"] if sel_path.exists() else None
|
| 109 |
+
jobs = []
|
| 110 |
+
for npy in sorted((root / split).glob("*/*.npy")):
|
| 111 |
+
key = str(npy.with_suffix("").relative_to(root))
|
| 112 |
+
jobs.append((str(npy), None if sel is None else sel.get(key, [])))
|
| 113 |
+
sub_k: dict[str, np.ndarray] = defaultdict(lambda: np.zeros(4**K, dtype=np.int64))
|
| 114 |
+
sub_c: dict[str, np.ndarray] = defaultdict(lambda: np.zeros(8, dtype=np.int64))
|
| 115 |
+
dom_c: dict[str, np.ndarray] = defaultdict(lambda: np.zeros(8, dtype=np.int64))
|
| 116 |
+
with ProcessPoolExecutor(workers) as ex:
|
| 117 |
+
for res in ex.map(shard_stats, *zip(*jobs), chunksize=4):
|
| 118 |
+
sub_k[res["subset"]] += res["kmer"]
|
| 119 |
+
for d, v in res["domains"].items():
|
| 120 |
+
sub_c[res["subset"]] += np.array(v)
|
| 121 |
+
dom_c[d] += np.array(v)
|
| 122 |
+
allk = sum(sub_k.values()) if sub_k else np.zeros(4**K, dtype=np.int64)
|
| 123 |
+
allc = sum(sub_c.values()) if sub_c else np.zeros(8, dtype=np.int64)
|
| 124 |
+
out_dir = root / "stats"
|
| 125 |
+
out_dir.mkdir(exist_ok=True)
|
| 126 |
+
np.savez_compressed(out_dir / f"{split}_kmer8.npz", overall=allk, **{f"subset_{k}": v for k, v in sub_k.items()})
|
| 127 |
+
st = {"split": split, "selected": sel is not None, "overall": {**base_summary(allc), **kmer_summary(allk)},
|
| 128 |
+
"subsets": {k: {**base_summary(sub_c[k]), **kmer_summary(sub_k[k])}
|
| 129 |
+
for k in sorted(sub_c, key=lambda s: -sub_c[s][:5].sum())},
|
| 130 |
+
"domains": {k: base_summary(v) for k, v in sorted(dom_c.items(), key=lambda kv: -kv[1][:5].sum())}}
|
| 131 |
+
(out_dir / f"{split}.json").write_text(json.dumps(st, indent=1))
|
| 132 |
+
o = st["overall"]
|
| 133 |
+
print(f"{split}: {o['tokens'] / 1e9:.3f}B tokens, GC {o['gc']:.4f}, soft-masked {o['soft_masked']:.4f}, "
|
| 134 |
+
f"CpG o/e {o['cpg_obs_exp']:.3f}, h_k " + " ".join(f"{v:.3f}" for v in o.get("markov_bits_per_base", {}).values()))
|
| 135 |
+
return st
|
| 136 |
+
|
| 137 |
+
|
| 138 |
+
def main() -> None:
|
| 139 |
+
ap = argparse.ArgumentParser(description=__doc__, formatter_class=argparse.RawDescriptionHelpFormatter)
|
| 140 |
+
ap.add_argument("--root", type=Path, default=Path("."))
|
| 141 |
+
ap.add_argument("--splits", nargs="+", default=["train", "heldout", "valid"])
|
| 142 |
+
ap.add_argument("--workers", type=int, default=8)
|
| 143 |
+
a = ap.parse_args()
|
| 144 |
+
for split in a.splits:
|
| 145 |
+
if (a.root / split).exists():
|
| 146 |
+
split_stats(a.root, split, a.workers)
|
| 147 |
+
|
| 148 |
+
|
| 149 |
+
if __name__ == "__main__":
|
| 150 |
+
main()
|
scripts/og2subset.py
ADDED
|
@@ -0,0 +1,397 @@
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|
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|
|
|
| 1 |
+
"""Build a small subset of OpenGenome2 (the Evo 2 training data) with the full dataset's variety.
|
| 2 |
+
|
| 3 |
+
Standalone: needs only numpy, requests and huggingface_hub (see requirements.txt).
|
| 4 |
+
|
| 5 |
+
Deterministic: the output depends only on (--revision, --seed, --rate or target, --window, --holdout, --rule):
|
| 6 |
+
each shard's generator is seeded by blake2b(seed, shard path) and each record's by (that, record index), so
|
| 7 |
+
worker count and completion order do not matter; held-out species are a seed-independent hash of the species
|
| 8 |
+
name; the source is pinned to an OpenGenome2 Hub commit (REVISION) and every plan/.done file records it.
|
| 9 |
+
|
| 10 |
+
python og2subset.py sample --split train --target-tokens 10e9 --holdout 0.01 [--workers 14] [--window 16384]
|
| 11 |
+
python og2subset.py sample --split valid --target-tokens 100e6
|
| 12 |
+
python og2subset.py finalize --split train [--target-tokens N] # keep all, or trim to N tokens
|
| 13 |
+
python og2subset.py finalize --split heldout
|
| 14 |
+
python og2subset.py report --split train
|
| 15 |
+
|
| 16 |
+
How a window is chosen (`sample`)
|
| 17 |
+
Every json/<phase>/<subset>/*_<split>_*.jsonl.gz shard of arcinstitute/opengenome2 is streamed over HTTP and
|
| 18 |
+
decompressed in memory; nothing but the sample is written, so the 2.8 TB of JSONL never touches the disk. Each
|
| 19 |
+
record's "text" is cut into segments at phylogenetic tags (|D__...;S__...|), contig separators (#) and window
|
| 20 |
+
separators (@); a segment is tiled into windows of --window bases on a fixed grid (the last one shorter), and a
|
| 21 |
+
window is kept iff u < rate, u ~ U(0, 1) from a generator seeded by (seed, shard, record index), whatever its
|
| 22 |
+
length. So every base has the same inclusion probability `rate` (short sequences such as promoters and ncRNA
|
| 23 |
+
included): the sample reproduces the composition of the full dataset (subsets, taxa, GC, repeats) in expectation,
|
| 24 |
+
and the same seed gives the same sample.
|
| 25 |
+
rate = target * oversample / (compressed bytes * tokens per byte). OpenGenome2's train JSONL holds ~9.24T
|
| 26 |
+
characters in 2.787 TB of gzip, i.e. ~3.32 tokens per compressed byte (the default).
|
| 27 |
+
|
| 28 |
+
Held-out species (`--holdout h`)
|
| 29 |
+
Records whose species (tag S__) hashes (blake2b, independent of the seed) below h go to <out>/heldout instead of
|
| 30 |
+
<out>/train, at the same rate; untagged records (metagenomes, transcripts, ...) are held out record by record. All
|
| 31 |
+
records of a species go the same way across shards and subsets, so heldout shares no species with train.
|
| 32 |
+
|
| 33 |
+
Output, per source shard: <out>/<split>/<subset>/<shard>.npy (uint8 codes of the kept windows, concatenated),
|
| 34 |
+
<shard>.jsonl (one line per window: offset, len, u, tag, rec) and <shard>.done (stats; makes `sample` resumable).
|
| 35 |
+
`finalize` writes <out>/<split>/selected.json (kept window ids per shard) and manifest.json (composition).
|
| 36 |
+
|
| 37 |
+
Encoding: one uint8 per base, low 3 bits A=0 C=1 G=2 T=3 N/other=4, bit 7 (0x80) set for lowercase (soft-masked).
|
| 38 |
+
"""
|
| 39 |
+
|
| 40 |
+
from __future__ import annotations
|
| 41 |
+
|
| 42 |
+
import argparse
|
| 43 |
+
import hashlib
|
| 44 |
+
import json
|
| 45 |
+
import re
|
| 46 |
+
import sys
|
| 47 |
+
import time
|
| 48 |
+
import zlib
|
| 49 |
+
from collections import Counter, defaultdict
|
| 50 |
+
from collections.abc import Iterator
|
| 51 |
+
from concurrent.futures import ProcessPoolExecutor, as_completed
|
| 52 |
+
from dataclasses import dataclass
|
| 53 |
+
from pathlib import Path
|
| 54 |
+
|
| 55 |
+
import numpy as np
|
| 56 |
+
|
| 57 |
+
HF_REPO = "arcinstitute/opengenome2"
|
| 58 |
+
# Pinned OpenGenome2 revision (Hub commit, last modified 2026-09-02) that this dataset was built from.
|
| 59 |
+
REVISION = "84d2a7e690c8bb395d1f4868822dfa031ea561c6"
|
| 60 |
+
BASE = f"https://huggingface.co/datasets/{HF_REPO}/resolve/"
|
| 61 |
+
TAG = re.compile(r"\|[^|]*\|")
|
| 62 |
+
SPLIT = re.compile(r"[#@]+")
|
| 63 |
+
TOKENS_PER_BYTE = 3.316 # measured: 9.242T characters in 2.787 TB of train JSONL gzip
|
| 64 |
+
|
| 65 |
+
# ---------------------------------------------------------------- encoding (same as acgt.data.tokenize)
|
| 66 |
+
N_ID, LOWER_BIT, BASE_MASK = 4, 0x80, 0x07
|
| 67 |
+
ENC = np.full(256, N_ID, dtype=np.uint8)
|
| 68 |
+
for _i, _c in enumerate(b"ACGT"):
|
| 69 |
+
ENC[_c] = _i
|
| 70 |
+
ENC[_c + 32] = _i | LOWER_BIT
|
| 71 |
+
for _c in range(ord("a"), ord("z") + 1):
|
| 72 |
+
if ENC[_c] == N_ID:
|
| 73 |
+
ENC[_c] = N_ID | LOWER_BIT
|
| 74 |
+
|
| 75 |
+
|
| 76 |
+
def encode(seq: str) -> np.ndarray:
|
| 77 |
+
return ENC[np.frombuffer(seq.encode(), dtype=np.uint8)]
|
| 78 |
+
|
| 79 |
+
|
| 80 |
+
def decode(arr: np.ndarray) -> str:
|
| 81 |
+
up, low = np.frombuffer(b"ACGTN", dtype=np.uint8), np.frombuffer(b"acgtn", dtype=np.uint8)
|
| 82 |
+
b = arr & BASE_MASK
|
| 83 |
+
return np.where((arr & LOWER_BIT) != 0, low[b], up[b]).astype(np.uint8).tobytes().decode()
|
| 84 |
+
|
| 85 |
+
|
| 86 |
+
# ---------------------------------------------------------------- records -> windows
|
| 87 |
+
@dataclass
|
| 88 |
+
class Window:
|
| 89 |
+
seq: str
|
| 90 |
+
u: float
|
| 91 |
+
tag: str
|
| 92 |
+
|
| 93 |
+
|
| 94 |
+
def shard_seed(seed: int, shard: str) -> int:
|
| 95 |
+
return int.from_bytes(hashlib.blake2b(f"{seed}:{shard}".encode(), digest_size=8).digest(), "little")
|
| 96 |
+
|
| 97 |
+
|
| 98 |
+
def record_tag(text: str) -> str:
|
| 99 |
+
"""The first taxonomy tag of a record ('' if untagged); it also labels the sequence before that tag."""
|
| 100 |
+
m = TAG.search(text)
|
| 101 |
+
return m.group(0)[1:-1] if m else ""
|
| 102 |
+
|
| 103 |
+
|
| 104 |
+
def species_of(tag: str) -> str:
|
| 105 |
+
m = re.search(r"S__([^;|]*)", tag)
|
| 106 |
+
return m.group(1) if m else ""
|
| 107 |
+
|
| 108 |
+
|
| 109 |
+
def unit_hash(key: str) -> float:
|
| 110 |
+
"""Deterministic U(0, 1) from a string (blake2b), independent of the sampling seed."""
|
| 111 |
+
return int.from_bytes(hashlib.blake2b(key.encode(), digest_size=8).digest(), "little") / 2**64
|
| 112 |
+
|
| 113 |
+
|
| 114 |
+
def holdout_key(text: str, shard: str, rec_idx: int) -> str:
|
| 115 |
+
"""Held-out unit of a record: its species when tagged, else the record itself."""
|
| 116 |
+
sp = species_of(record_tag(text))
|
| 117 |
+
return f"S:{sp}" if sp else f"R:{shard}:{rec_idx}"
|
| 118 |
+
|
| 119 |
+
|
| 120 |
+
def segments(text: str, tag: str = "") -> Iterator[tuple[str, str]]:
|
| 121 |
+
"""(sequence segment, taxonomy tag in force) pairs: tags and #/@ separators are boundaries, never content.
|
| 122 |
+
`tag` labels the sequence before the first tag."""
|
| 123 |
+
pos = 0
|
| 124 |
+
for m in TAG.finditer(text):
|
| 125 |
+
yield from ((s, tag) for s in SPLIT.split(text[pos:m.start()]) if s)
|
| 126 |
+
tag = m.group(0)[1:-1]
|
| 127 |
+
pos = m.end()
|
| 128 |
+
yield from ((s, tag) for s in SPLIT.split(text[pos:]) if s)
|
| 129 |
+
|
| 130 |
+
|
| 131 |
+
def sample_record(text: str, rng: np.random.Generator, rate: float, window: int,
|
| 132 |
+
rule: str = "per_base") -> list[Window]:
|
| 133 |
+
"""Windows of one record, each kept with probability `rate` whatever its length: every base has inclusion
|
| 134 |
+
probability `rate`. (Keeping a window with probability rate * len / window would under-sample short sequences
|
| 135 |
+
by len / window; that rule is kept as rule="length_weighted" only to reproduce data sampled with it.)"""
|
| 136 |
+
if rule not in ("per_base", "length_weighted"):
|
| 137 |
+
raise ValueError(f"unknown rule {rule!r}")
|
| 138 |
+
out = []
|
| 139 |
+
for seg, tag in segments(text, record_tag(text)):
|
| 140 |
+
n = len(seg)
|
| 141 |
+
starts = range(0, n, window)
|
| 142 |
+
u = rng.random(len(starts))
|
| 143 |
+
for k, s in enumerate(starts):
|
| 144 |
+
L = min(window, n - s)
|
| 145 |
+
if u[k] < (rate if rule == "per_base" else rate * L / window):
|
| 146 |
+
out.append(Window(seg[s:s + L], float(u[k]), tag))
|
| 147 |
+
return out
|
| 148 |
+
|
| 149 |
+
|
| 150 |
+
# ---------------------------------------------------------------- streaming
|
| 151 |
+
def iter_lines(chunks: Iterator[bytes]) -> Iterator[bytes]:
|
| 152 |
+
"""Lines of a gzip stream given as byte chunks; one split per chunk (records range from 100 b to ~20 Mb)."""
|
| 153 |
+
dec = zlib.decompressobj(16 + zlib.MAX_WBITS)
|
| 154 |
+
parts: list[bytes] = []
|
| 155 |
+
for chunk in chunks:
|
| 156 |
+
data = dec.decompress(chunk)
|
| 157 |
+
if not data:
|
| 158 |
+
continue
|
| 159 |
+
pieces = data.split(b"\n")
|
| 160 |
+
if len(pieces) == 1:
|
| 161 |
+
parts.append(data)
|
| 162 |
+
continue
|
| 163 |
+
parts.append(pieces[0])
|
| 164 |
+
yield b"".join(parts)
|
| 165 |
+
yield from pieces[1:-1]
|
| 166 |
+
parts = [pieces[-1]] if pieces[-1] else []
|
| 167 |
+
tail = dec.flush()
|
| 168 |
+
if tail:
|
| 169 |
+
parts.append(tail)
|
| 170 |
+
if parts and b"".join(parts).strip():
|
| 171 |
+
yield b"".join(parts)
|
| 172 |
+
|
| 173 |
+
|
| 174 |
+
def http_chunks(path: str, revision: str = REVISION, chunk_size: int = 1 << 20) -> Iterator[bytes]:
|
| 175 |
+
import requests
|
| 176 |
+
|
| 177 |
+
with requests.get(f"{BASE}{revision}/{path}", stream=True, timeout=120) as r:
|
| 178 |
+
r.raise_for_status()
|
| 179 |
+
yield from r.iter_content(chunk_size=chunk_size)
|
| 180 |
+
|
| 181 |
+
|
| 182 |
+
def file_chunks(path: Path, chunk_size: int = 1 << 20) -> Iterator[bytes]:
|
| 183 |
+
with open(path, "rb") as f:
|
| 184 |
+
while chunk := f.read(chunk_size):
|
| 185 |
+
yield chunk
|
| 186 |
+
|
| 187 |
+
|
| 188 |
+
def shard_out(out_dir: Path, split: str, shard: str) -> Path:
|
| 189 |
+
"""<out>/<split>/<subset>/<shard file stem>; the subset is the directory under json/<phase>/."""
|
| 190 |
+
parts = shard.split("/")
|
| 191 |
+
subset = parts[2] if len(parts) > 3 else "misc"
|
| 192 |
+
return out_dir / split / subset / Path(parts[-1]).name.removesuffix(".jsonl.gz")
|
| 193 |
+
|
| 194 |
+
|
| 195 |
+
class _Writer:
|
| 196 |
+
def __init__(self) -> None:
|
| 197 |
+
self.codes: list[np.ndarray] = []
|
| 198 |
+
self.index: list[dict] = []
|
| 199 |
+
self.offset = self.records = self.chars = 0
|
| 200 |
+
|
| 201 |
+
def add(self, w: Window, rec_idx: int) -> None:
|
| 202 |
+
self.codes.append(encode(w.seq))
|
| 203 |
+
self.index.append({"offset": self.offset, "len": len(w.seq), "u": round(w.u, 9), "tag": w.tag, "rec": rec_idx})
|
| 204 |
+
self.offset += len(w.seq)
|
| 205 |
+
|
| 206 |
+
def save(self, base: Path, stats: dict) -> dict:
|
| 207 |
+
base.parent.mkdir(parents=True, exist_ok=True)
|
| 208 |
+
arr = np.concatenate(self.codes) if self.codes else np.zeros(0, dtype=np.uint8)
|
| 209 |
+
np.save(base.with_suffix(".npy"), arr)
|
| 210 |
+
base.with_suffix(".jsonl").write_text("".join(json.dumps(r) + "\n" for r in self.index))
|
| 211 |
+
stats = {**stats, "records": self.records, "chars_seen": self.chars, "kept_windows": len(self.index),
|
| 212 |
+
"kept_bases": int(self.offset)}
|
| 213 |
+
base.with_suffix(".done").write_text(json.dumps(stats))
|
| 214 |
+
return stats
|
| 215 |
+
|
| 216 |
+
|
| 217 |
+
def sample_shard(shard: str, out_dir: Path, split: str, rate: float, window: int, seed: int,
|
| 218 |
+
chunks: Iterator[bytes] | None = None, holdout: float = 0.0, holdout_split: str = "heldout",
|
| 219 |
+
rule: str = "per_base", revision: str = REVISION) -> dict:
|
| 220 |
+
"""Stream one shard and write its kept windows (and held-out records'). Returns stats; skips shards done."""
|
| 221 |
+
base = shard_out(out_dir, split, shard)
|
| 222 |
+
done = base.with_suffix(".done")
|
| 223 |
+
if done.exists():
|
| 224 |
+
return json.loads(done.read_text())
|
| 225 |
+
t0 = time.time()
|
| 226 |
+
root = shard_seed(seed, shard)
|
| 227 |
+
main, held = _Writer(), _Writer()
|
| 228 |
+
for rec_idx, line in enumerate(iter_lines(chunks if chunks is not None else http_chunks(shard, revision))):
|
| 229 |
+
text = json.loads(line)["text"]
|
| 230 |
+
wr = held if holdout > 0 and unit_hash(holdout_key(text, shard, rec_idx)) < holdout else main
|
| 231 |
+
wr.records += 1
|
| 232 |
+
wr.chars += len(text) # incl. tag characters; an upper bound on bases
|
| 233 |
+
rng = np.random.default_rng([root, rec_idx])
|
| 234 |
+
for w in sample_record(text, rng, rate, window, rule):
|
| 235 |
+
wr.add(w, rec_idx)
|
| 236 |
+
common = {"shard": shard, "rate": rate, "window": window, "seed": seed, "holdout": holdout, "rule": rule,
|
| 237 |
+
"revision": revision}
|
| 238 |
+
if holdout > 0:
|
| 239 |
+
held.save(shard_out(out_dir, holdout_split, shard), {**common, "split": holdout_split})
|
| 240 |
+
return main.save(base, {**common, "split": split, "seconds": round(time.time() - t0, 1)})
|
| 241 |
+
|
| 242 |
+
|
| 243 |
+
# ---------------------------------------------------------------- CLI
|
| 244 |
+
def list_shards(split: str, subsets: list[str] | None, revision: str = REVISION) -> list[tuple[str, int]]:
|
| 245 |
+
from huggingface_hub import HfApi
|
| 246 |
+
|
| 247 |
+
out = []
|
| 248 |
+
for f in HfApi().list_repo_tree(HF_REPO, repo_type="dataset", recursive=True, revision=revision):
|
| 249 |
+
p, size = f.path, getattr(f, "size", None)
|
| 250 |
+
if size is None or not p.startswith("json/") or not p.endswith(".jsonl.gz"):
|
| 251 |
+
continue
|
| 252 |
+
if not re.search(rf"[_/]{split}[_.]", p.rsplit("/", 1)[-1]):
|
| 253 |
+
continue
|
| 254 |
+
if subsets and p.split("/")[2] not in subsets:
|
| 255 |
+
continue
|
| 256 |
+
out.append((p, int(size)))
|
| 257 |
+
return sorted(out, key=lambda x: -x[1])
|
| 258 |
+
|
| 259 |
+
|
| 260 |
+
def run_one(shard: str, out: str, split: str, rate: float, window: int, seed: int, holdout: float, rule: str,
|
| 261 |
+
revision: str, retries: int = 4) -> dict:
|
| 262 |
+
for attempt in range(retries):
|
| 263 |
+
try:
|
| 264 |
+
return sample_shard(shard, Path(out), split, rate, window, seed, holdout=holdout, rule=rule,
|
| 265 |
+
revision=revision)
|
| 266 |
+
except Exception as e: # noqa: BLE001 network hiccup mid-stream (any error): start the shard again
|
| 267 |
+
if attempt == retries - 1:
|
| 268 |
+
return {"shard": shard, "error": repr(e)}
|
| 269 |
+
time.sleep(10 * (attempt + 1))
|
| 270 |
+
raise AssertionError
|
| 271 |
+
|
| 272 |
+
|
| 273 |
+
def cmd_sample(a) -> None:
|
| 274 |
+
shards = list_shards(a.split, a.subsets, a.revision)
|
| 275 |
+
if a.limit:
|
| 276 |
+
shards = shards[-a.limit:]
|
| 277 |
+
total_bytes = sum(s for _, s in shards)
|
| 278 |
+
target = a.target_tokens or 10e9
|
| 279 |
+
rate = a.rate if a.rate else min(1.0, target * a.oversample / (total_bytes * a.tokens_per_byte))
|
| 280 |
+
root = Path(a.out)
|
| 281 |
+
root.mkdir(parents=True, exist_ok=True)
|
| 282 |
+
plan = {"split": a.split, "target_tokens": target, "oversample": a.oversample, "rate": rate, "window": a.window,
|
| 283 |
+
"seed": a.seed, "holdout": a.holdout, "shards": len(shards), "compressed_bytes": total_bytes,
|
| 284 |
+
"tokens_per_byte_assumed": a.tokens_per_byte}
|
| 285 |
+
plan |= {"rule": a.rule, "subsets": a.subsets or "all", "revision": a.revision, "source": HF_REPO}
|
| 286 |
+
(root / (a.plan or f"plan_{a.split}.json")).write_text(json.dumps(plan, indent=1))
|
| 287 |
+
print(json.dumps(plan), flush=True)
|
| 288 |
+
t0, done_bytes, kept = time.time(), 0, 0
|
| 289 |
+
sizes = dict(shards)
|
| 290 |
+
with ProcessPoolExecutor(a.workers) as ex:
|
| 291 |
+
futs = [ex.submit(run_one, p, a.out, a.split, rate, a.window, a.seed, a.holdout, a.rule, a.revision) for p, _ in shards]
|
| 292 |
+
for i, fut in enumerate(as_completed(futs), 1):
|
| 293 |
+
st = fut.result()
|
| 294 |
+
done_bytes += sizes[st["shard"]]
|
| 295 |
+
kept += st.get("kept_bases", 0)
|
| 296 |
+
el = time.time() - t0
|
| 297 |
+
print(f"[{i}/{len(shards)}] {done_bytes / 1e9:.0f}/{total_bytes / 1e9:.0f} GB kept {kept / 1e9:.3f}B "
|
| 298 |
+
f"{el / 3600:.2f} h {st['shard'].rsplit('/', 1)[-1]}" + (f" ERROR {st['error']}" if "error" in st else ""),
|
| 299 |
+
flush=True)
|
| 300 |
+
|
| 301 |
+
|
| 302 |
+
def load_index(root: Path, split: str) -> list[tuple[Path, list[dict], dict]]:
|
| 303 |
+
out = []
|
| 304 |
+
for done in sorted((root / split).glob("*/*.done")):
|
| 305 |
+
rows = [json.loads(line) for line in done.with_suffix(".jsonl").read_text().splitlines()]
|
| 306 |
+
out.append((done.with_suffix(""), rows, json.loads(done.read_text())))
|
| 307 |
+
return out
|
| 308 |
+
|
| 309 |
+
|
| 310 |
+
def cmd_finalize(a) -> None:
|
| 311 |
+
root = Path(a.out)
|
| 312 |
+
idx = load_index(root, a.split)
|
| 313 |
+
kept = sum(r["len"] for _, rows, _ in idx for r in rows)
|
| 314 |
+
rate, window = idx[0][2]["rate"], idx[0][2]["window"]
|
| 315 |
+
final_rate = rate * (min(1.0, a.target_tokens / kept) if a.target_tokens and kept else 1.0)
|
| 316 |
+
selected, total = {}, 0
|
| 317 |
+
for base, rows, _ in idx:
|
| 318 |
+
ids = [i for i, r in enumerate(rows) if r["u"] < final_rate]
|
| 319 |
+
selected[str(base.relative_to(root))] = ids
|
| 320 |
+
total += sum(rows[i]["len"] for i in ids)
|
| 321 |
+
(root / a.split / "selected.json").write_text(json.dumps({"rate": final_rate, "window": window, "tokens": total,
|
| 322 |
+
"shards": selected}))
|
| 323 |
+
print(f"{a.split}: kept {kept / 1e9:.3f}B -> selected {total / 1e9:.3f}B tokens (rate {final_rate:.3e})")
|
| 324 |
+
cmd_report(a)
|
| 325 |
+
|
| 326 |
+
|
| 327 |
+
def taxon(tag: str, rank: str) -> str:
|
| 328 |
+
m = re.search(rf"{rank}__([^;|]*)", tag)
|
| 329 |
+
return m.group(1) if m else "(untagged)"
|
| 330 |
+
|
| 331 |
+
|
| 332 |
+
def cmd_report(a) -> None:
|
| 333 |
+
root = Path(a.out)
|
| 334 |
+
idx = load_index(root, a.split)
|
| 335 |
+
sel_path = root / a.split / "selected.json"
|
| 336 |
+
sel = json.loads(sel_path.read_text())["shards"] if sel_path.exists() else None
|
| 337 |
+
by_subset, by_domain, by_phylum, windows = Counter(), Counter(), Counter(), Counter()
|
| 338 |
+
species: dict[str, set] = defaultdict(set)
|
| 339 |
+
seen_chars, records = Counter(), Counter()
|
| 340 |
+
for base, rows, st in idx:
|
| 341 |
+
subset = base.parent.name
|
| 342 |
+
seen_chars[subset] += st["chars_seen"]
|
| 343 |
+
records[subset] += st["records"]
|
| 344 |
+
ids = sel[str(base.relative_to(root))] if sel is not None else range(len(rows))
|
| 345 |
+
for i in ids:
|
| 346 |
+
r = rows[i]
|
| 347 |
+
by_subset[subset] += r["len"]
|
| 348 |
+
windows[subset] += 1
|
| 349 |
+
by_domain[taxon(r["tag"], "D")] += r["len"]
|
| 350 |
+
by_phylum[taxon(r["tag"], "P")] += r["len"]
|
| 351 |
+
if r["tag"]:
|
| 352 |
+
species[subset].add(taxon(r["tag"], "S"))
|
| 353 |
+
total = sum(by_subset.values())
|
| 354 |
+
seen = sum(seen_chars.values())
|
| 355 |
+
rep = {"split": a.split, "tokens": total, "windows": sum(windows.values()), "selected": sel is not None,
|
| 356 |
+
"subsets": {k: {"tokens": v, "windows": windows[k], "share": v / total, "full_share": seen_chars[k] / seen,
|
| 357 |
+
"records_seen": records[k], "chars_seen": seen_chars[k], "species": len(species[k])}
|
| 358 |
+
for k, v in by_subset.most_common()},
|
| 359 |
+
"domains": {k: v / total for k, v in by_domain.most_common()},
|
| 360 |
+
"phyla_top30": {k: v / total for k, v in by_phylum.most_common(30)},
|
| 361 |
+
"n_phyla": len(by_phylum), "n_species": len(set().union(*species.values())) if species else 0,
|
| 362 |
+
"chars_seen": seen, "records_seen": sum(records.values())}
|
| 363 |
+
(root / a.split / "manifest.json").write_text(json.dumps(rep, indent=1))
|
| 364 |
+
print(f"{a.split}: {total / 1e9:.3f}B tokens in {rep['windows']} windows, from {seen / 1e12:.3f}T characters "
|
| 365 |
+
f"seen; {rep['n_species']} species, {rep['n_phyla']} phyla")
|
| 366 |
+
for k, v in rep["subsets"].items():
|
| 367 |
+
print(f" {k:<28} {v['tokens'] / 1e9:8.3f}B share {v['share']:.4f} full {v['full_share']:.4f} "
|
| 368 |
+
f"species {v['species']}")
|
| 369 |
+
|
| 370 |
+
|
| 371 |
+
def main() -> None:
|
| 372 |
+
ap = argparse.ArgumentParser(description=__doc__, formatter_class=argparse.RawDescriptionHelpFormatter)
|
| 373 |
+
ap.add_argument("cmd", choices=["sample", "finalize", "report"])
|
| 374 |
+
ap.add_argument("--split", default="train", choices=["train", "valid", "test", "heldout"],
|
| 375 |
+
help="sample: the source split to stream; finalize/report: an output split (incl. heldout)")
|
| 376 |
+
ap.add_argument("--holdout", type=float, default=0.0, help="sample: fraction of species sent to <out>/heldout")
|
| 377 |
+
ap.add_argument("--target-tokens", type=float, default=None,
|
| 378 |
+
help="sample: size target (default 10e9); finalize: trim to this many tokens (default: keep all)")
|
| 379 |
+
ap.add_argument("--oversample", type=float, default=1.0, help="sample this much extra (trim with finalize)")
|
| 380 |
+
ap.add_argument("--rate", type=float, default=None, help="sample: inclusion rate per base (overrides the target)")
|
| 381 |
+
ap.add_argument("--rule", default="per_base", choices=["per_base", "length_weighted"],
|
| 382 |
+
help="sample: per_base (correct); length_weighted only to reproduce data sampled with it")
|
| 383 |
+
ap.add_argument("--plan", default=None, help="sample: plan file name (default plan_<split>.json)")
|
| 384 |
+
ap.add_argument("--revision", default=REVISION, help="OpenGenome2 Hub revision (commit) to read; pinned by default")
|
| 385 |
+
ap.add_argument("--tokens-per-byte", type=float, default=TOKENS_PER_BYTE)
|
| 386 |
+
ap.add_argument("--window", type=int, default=16384)
|
| 387 |
+
ap.add_argument("--workers", type=int, default=8)
|
| 388 |
+
ap.add_argument("--seed", type=int, default=0)
|
| 389 |
+
ap.add_argument("--subsets", nargs="*", default=None, help="restrict to these subsets (directory names)")
|
| 390 |
+
ap.add_argument("--limit", type=int, default=0, help="sample: only the N smallest shards (trial)")
|
| 391 |
+
ap.add_argument("--out", default=".", help="dataset root (default: current directory)")
|
| 392 |
+
a = ap.parse_args()
|
| 393 |
+
{"sample": cmd_sample, "finalize": cmd_finalize, "report": cmd_report}[a.cmd](a)
|
| 394 |
+
|
| 395 |
+
|
| 396 |
+
if __name__ == "__main__":
|
| 397 |
+
sys.exit(main())
|
scripts/requirements.txt
ADDED
|
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
numpy>=1.24
|
| 2 |
+
requests>=2.28
|
| 3 |
+
huggingface_hub>=0.20
|
scripts/species_overlap.py
ADDED
|
@@ -0,0 +1,45 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
"""Species overlap between splits (S__ tags of the kept windows): how many of a split's species also occur in train.
|
| 2 |
+
|
| 3 |
+
python species_overlap.py [--root .] [--splits heldout valid] [--ref train]
|
| 4 |
+
"""
|
| 5 |
+
|
| 6 |
+
from __future__ import annotations
|
| 7 |
+
|
| 8 |
+
import argparse
|
| 9 |
+
import json
|
| 10 |
+
import re
|
| 11 |
+
from pathlib import Path
|
| 12 |
+
|
| 13 |
+
|
| 14 |
+
def species(root: Path, split: str) -> dict[str, int]:
|
| 15 |
+
"""species -> bases in the split's selected windows."""
|
| 16 |
+
sel_path = root / split / "selected.json"
|
| 17 |
+
sel = json.loads(sel_path.read_text())["shards"] if sel_path.exists() else None
|
| 18 |
+
out: dict[str, int] = {}
|
| 19 |
+
for jl in sorted((root / split).glob("*/*.jsonl")):
|
| 20 |
+
rows = [json.loads(line) for line in jl.read_text().splitlines()]
|
| 21 |
+
ids = sel.get(str(jl.with_suffix("").relative_to(root)), []) if sel is not None else range(len(rows))
|
| 22 |
+
for i in ids:
|
| 23 |
+
m = re.search(r"S__([^;|]*)", rows[i]["tag"])
|
| 24 |
+
if m:
|
| 25 |
+
out[m.group(1)] = out.get(m.group(1), 0) + rows[i]["len"]
|
| 26 |
+
return out
|
| 27 |
+
|
| 28 |
+
|
| 29 |
+
def main() -> None:
|
| 30 |
+
ap = argparse.ArgumentParser()
|
| 31 |
+
ap.add_argument("--root", type=Path, default=Path("."))
|
| 32 |
+
ap.add_argument("--splits", nargs="+", default=["heldout", "valid"])
|
| 33 |
+
ap.add_argument("--ref", default="train")
|
| 34 |
+
a = ap.parse_args()
|
| 35 |
+
ref = species(a.root, a.ref)
|
| 36 |
+
for split in a.splits:
|
| 37 |
+
sp = species(a.root, split)
|
| 38 |
+
shared = set(sp) & set(ref)
|
| 39 |
+
tagged = sum(sp.values())
|
| 40 |
+
print(f"{split}: {len(sp)} species, {len(shared)} also in {a.ref} "
|
| 41 |
+
f"({sum(sp[s] for s in shared) / max(tagged, 1):.1%} of its tagged bases)")
|
| 42 |
+
|
| 43 |
+
|
| 44 |
+
if __name__ == "__main__":
|
| 45 |
+
main()
|
stats/baseline.json
ADDED
|
@@ -0,0 +1,613 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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| 1 |
+
{
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| 2 |
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"fit": "train",
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| 3 |
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| 4 |
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| 5 |
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| 6 |
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|
| 7 |
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|
| 8 |
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|
| 9 |
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|
| 10 |
+
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|
| 11 |
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|
| 12 |
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|
| 13 |
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|
| 14 |
+
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| 15 |
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| 16 |
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| 17 |
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| 19 |
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| 22 |
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| 23 |
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| 24 |
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| 25 |
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| 26 |
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|
| 27 |
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| 28 |
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|
| 29 |
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| 30 |
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| 31 |
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| 32 |
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| 33 |
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| 34 |
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| 35 |
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| 91 |
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| 106 |
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"6": 1.98011,
|
| 560 |
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"7": 1.97614
|
| 561 |
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},
|
| 562 |
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"best_order": 7,
|
| 563 |
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"best": 1.97614
|
| 564 |
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},
|
| 565 |
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"imgpr": {
|
| 566 |
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"positions": 7945463,
|
| 567 |
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"bits_per_base": {
|
| 568 |
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"0": 2.04755,
|
| 569 |
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"1": 2.05208,
|
| 570 |
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"2": 2.04608,
|
| 571 |
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"3": 2.03019,
|
| 572 |
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"4": 2.0238,
|
| 573 |
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"5": 2.01913,
|
| 574 |
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"6": 2.0046,
|
| 575 |
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"7": 1.9987
|
| 576 |
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},
|
| 577 |
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"best_order": 7,
|
| 578 |
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"best": 1.9987
|
| 579 |
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},
|
| 580 |
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"organelle": {
|
| 581 |
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"positions": 7945172,
|
| 582 |
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"bits_per_base": {
|
| 583 |
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|
| 584 |
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"1": 1.95211,
|
| 585 |
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"2": 1.9531,
|
| 586 |
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"3": 1.95334,
|
| 587 |
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"4": 1.95213,
|
| 588 |
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"5": 1.95119,
|
| 589 |
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"6": 1.94825,
|
| 590 |
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"7": 1.94801
|
| 591 |
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},
|
| 592 |
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"best_order": 7,
|
| 593 |
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"best": 1.94801
|
| 594 |
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},
|
| 595 |
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"promoters": {
|
| 596 |
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"positions": 247272,
|
| 597 |
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"bits_per_base": {
|
| 598 |
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"0": 2.00075,
|
| 599 |
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"1": 1.98879,
|
| 600 |
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"2": 1.9733,
|
| 601 |
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"3": 1.96176,
|
| 602 |
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"4": 1.95246,
|
| 603 |
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"5": 1.9447,
|
| 604 |
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"6": 1.93239,
|
| 605 |
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"7": 1.92649
|
| 606 |
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},
|
| 607 |
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"best_order": 7,
|
| 608 |
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"best": 1.92649
|
| 609 |
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}
|
| 610 |
+
}
|
| 611 |
+
}
|
| 612 |
+
}
|
| 613 |
+
}
|
stats/heldout.json
ADDED
|
@@ -0,0 +1,1611 @@
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|
| 1 |
+
{
|
| 2 |
+
"split": "heldout",
|
| 3 |
+
"selected": true,
|
| 4 |
+
"overall": {
|
| 5 |
+
"tokens": 124469222,
|
| 6 |
+
"windows": 15940,
|
| 7 |
+
"freq": {
|
| 8 |
+
"A": 0.2874923810482241,
|
| 9 |
+
"C": 0.21234394796811698,
|
| 10 |
+
"G": 0.21214434842374125,
|
| 11 |
+
"T": 0.28795695372788627,
|
| 12 |
+
"N": 6.23688320314238e-05
|
| 13 |
+
},
|
| 14 |
+
"gc": 0.424514772882423,
|
| 15 |
+
"soft_masked": 0.34792344889887716,
|
| 16 |
+
"cpg_obs_exp": 0.7594313539790454,
|
| 17 |
+
"kmers_counted": 124329163,
|
| 18 |
+
"distinct_8mers": 65536,
|
| 19 |
+
"entropy_bits": {
|
| 20 |
+
"1": 1.98349,
|
| 21 |
+
"2": 3.95624,
|
| 22 |
+
"3": 5.92369,
|
| 23 |
+
"4": 7.88425,
|
| 24 |
+
"5": 9.83909,
|
| 25 |
+
"6": 11.78895,
|
| 26 |
+
"7": 13.72968,
|
| 27 |
+
"8": 15.66411
|
| 28 |
+
},
|
| 29 |
+
"markov_bits_per_base": {
|
| 30 |
+
"0": 1.98349,
|
| 31 |
+
"1": 1.97275,
|
| 32 |
+
"2": 1.96745,
|
| 33 |
+
"3": 1.96055,
|
| 34 |
+
"4": 1.95484,
|
| 35 |
+
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| 698 |
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| 702 |
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| 703 |
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| 706 |
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| 710 |
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| 714 |
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| 715 |
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| 769 |
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| 777 |
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| 785 |
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| 789 |
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| 790 |
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| 793 |
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| 794 |
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| 796 |
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| 797 |
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| 798 |
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| 800 |
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| 801 |
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| 802 |
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| 804 |
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| 805 |
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| 806 |
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| 809 |
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| 810 |
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| 813 |
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| 814 |
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| 876 |
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| 879 |
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| 880 |
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| 884 |
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| 885 |
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| 887 |
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| 888 |
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| 889 |
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0.000309
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| 890 |
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| 891 |
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|
| 892 |
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|
| 893 |
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0.000285
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| 894 |
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| 895 |
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[
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| 896 |
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| 899 |
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| 900 |
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| 901 |
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| 903 |
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| 904 |
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| 905 |
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| 907 |
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| 908 |
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| 909 |
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| 912 |
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| 913 |
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| 915 |
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| 916 |
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| 917 |
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| 918 |
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| 919 |
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| 920 |
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| 921 |
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| 922 |
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| 923 |
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| 924 |
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| 925 |
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| 927 |
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| 928 |
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| 929 |
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| 930 |
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| 931 |
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| 932 |
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| 933 |
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0.00013
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| 934 |
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| 935 |
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[
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| 936 |
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| 937 |
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| 938 |
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| 939 |
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| 940 |
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| 941 |
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0.000126
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| 942 |
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| 943 |
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| 1611 |
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|
stats/train.json
ADDED
|
@@ -0,0 +1,1611 @@
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| 1 |
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| 1611 |
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}
|
stats/valid.json
ADDED
|
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|
|
|
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|
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|
|
| 1 |
+
{
|
| 2 |
+
"split": "valid",
|
| 3 |
+
"selected": true,
|
| 4 |
+
"overall": {
|
| 5 |
+
"tokens": 99061054,
|
| 6 |
+
"windows": 7811,
|
| 7 |
+
"freq": {
|
| 8 |
+
"A": 0.27757356589401927,
|
| 9 |
+
"C": 0.22217951567525215,
|
| 10 |
+
"G": 0.22258515440386895,
|
| 11 |
+
"T": 0.2776474900014692,
|
| 12 |
+
"N": 1.4274025390442545e-05
|
| 13 |
+
},
|
| 14 |
+
"gc": 0.4447710187519357,
|
| 15 |
+
"soft_masked": 0.25334512390712094,
|
| 16 |
+
"cpg_obs_exp": 0.9348297458353338,
|
| 17 |
+
"kmers_counted": 98998086,
|
| 18 |
+
"distinct_8mers": 65536,
|
| 19 |
+
"entropy_bits": {
|
| 20 |
+
"1": 1.99118,
|
| 21 |
+
"2": 3.97493,
|
| 22 |
+
"3": 5.95513,
|
| 23 |
+
"4": 7.92567,
|
| 24 |
+
"5": 9.89223,
|
| 25 |
+
"6": 11.85525,
|
| 26 |
+
"7": 13.81038,
|
| 27 |
+
"8": 15.76134
|
| 28 |
+
},
|
| 29 |
+
"markov_bits_per_base": {
|
| 30 |
+
"0": 1.99118,
|
| 31 |
+
"1": 1.98375,
|
| 32 |
+
"2": 1.9802,
|
| 33 |
+
"3": 1.97054,
|
| 34 |
+
"4": 1.96656,
|
| 35 |
+
"5": 1.96302,
|
| 36 |
+
"6": 1.95513,
|
| 37 |
+
"7": 1.95096
|
| 38 |
+
},
|
| 39 |
+
"chargaff2_4mer_asymmetry": 0.00237,
|
| 40 |
+
"top_8mers": [
|
| 41 |
+
[
|
| 42 |
+
"TTTTTTTT",
|
| 43 |
+
0.00069
|
| 44 |
+
],
|
| 45 |
+
[
|
| 46 |
+
"AAAAAAAA",
|
| 47 |
+
0.000689
|
| 48 |
+
],
|
| 49 |
+
[
|
| 50 |
+
"ATATATAT",
|
| 51 |
+
0.000386
|
| 52 |
+
],
|
| 53 |
+
[
|
| 54 |
+
"TATATATA",
|
| 55 |
+
0.00038
|
| 56 |
+
],
|
| 57 |
+
[
|
| 58 |
+
"AAAAAAAT",
|
| 59 |
+
0.000242
|
| 60 |
+
],
|
| 61 |
+
[
|
| 62 |
+
"ATTTTTTT",
|
| 63 |
+
0.000242
|
| 64 |
+
],
|
| 65 |
+
[
|
| 66 |
+
"TAAAAAAA",
|
| 67 |
+
0.0002
|
| 68 |
+
],
|
| 69 |
+
[
|
| 70 |
+
"AAAATAAA",
|
| 71 |
+
0.000199
|
| 72 |
+
],
|
| 73 |
+
[
|
| 74 |
+
"TTTTTTTA",
|
| 75 |
+
0.000197
|
| 76 |
+
],
|
| 77 |
+
[
|
| 78 |
+
"TTTATTTT",
|
| 79 |
+
0.000196
|
| 80 |
+
],
|
| 81 |
+
[
|
| 82 |
+
"AAATAAAA",
|
| 83 |
+
0.000195
|
| 84 |
+
],
|
| 85 |
+
[
|
| 86 |
+
"TTTTATTT",
|
| 87 |
+
0.000193
|
| 88 |
+
],
|
| 89 |
+
[
|
| 90 |
+
"TGTGTGTG",
|
| 91 |
+
0.000192
|
| 92 |
+
],
|
| 93 |
+
[
|
| 94 |
+
"GTGTGTGT",
|
| 95 |
+
0.000186
|
| 96 |
+
],
|
| 97 |
+
[
|
| 98 |
+
"CACACACA",
|
| 99 |
+
0.000183
|
| 100 |
+
],
|
| 101 |
+
[
|
| 102 |
+
"AATAATAA",
|
| 103 |
+
0.000182
|
| 104 |
+
],
|
| 105 |
+
[
|
| 106 |
+
"AAAAATAA",
|
| 107 |
+
0.000178
|
| 108 |
+
],
|
| 109 |
+
[
|
| 110 |
+
"TTATTATT",
|
| 111 |
+
0.000178
|
| 112 |
+
],
|
| 113 |
+
[
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| 114 |
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train/manifest.json
ADDED
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@@ -0,0 +1,158 @@
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},
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| 154 |
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| 155 |
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| 156 |
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| 157 |
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| 158 |
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}
|
train/selected.json
ADDED
|
The diff for this file is too large to render.
See raw diff
|
|
|
valid/manifest.json
ADDED
|
@@ -0,0 +1,151 @@
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|
| 1 |
+
{
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| 2 |
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| 3 |
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|
valid/selected.json
ADDED
|
@@ -0,0 +1 @@
|
|
|
|
|
|
|
| 1 |
+
{"rate": 0.06750641858365167, "window": 16384, "tokens": 99061054, "shards": {"valid/eukaryotic_genic_windows/windows_5kb_valid_chunk1": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68], "valid/gtdb_v220_imgpr/data_gtdb_valid_chunk1": [0, 1, 2, 3, 4, 5, 6, 7], "valid/gtdb_v220_stitched/data_gtdb_valid_chunk1": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111, 112, 113, 114, 115, 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