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The dataset generation failed because of a cast error
Error code:   DatasetGenerationCastError
Exception:    DatasetGenerationCastError
Message:      An error occurred while generating the dataset

All the data files must have the same columns, but at some point there are 17 new columns ({'seed_default_mono', 'seed_default_out', 'uDANCE_cost', 'seed_default_cost', 'seed_default_misplaced', 'uDANCE_in', 'seed_extended_in', 'uDANCE_misplaced', 'seed_default_in', 'uDANCE_out', 'seed_extended_cost', 'tips', 'uDANCE_mono', 'seed_extended_mono', 'rank', 'seed_extended_misplaced', 'seed_extended_out'}) and 5 missing columns ({'outside_clades', 'outside_group_taxa', 'main_group_taxa', 'pure_clades', 'tree'}).

This happened while the csv dataset builder was generating data using

hf://datasets/risenfromashes/qraft-experiments/runs/bdal-2026-10-23-chain-scores-and-seeded-gtdb/eval199k-seed/gtdb199k-groups.csv (at revision 2ec102aeb9b7bb5615c175f071e2b6d334a88ade), ['hf://datasets/risenfromashes/qraft-experiments@2ec102aeb9b7bb5615c175f071e2b6d334a88ade/runs/bdal-2026-10-23-chain-scores-and-seeded-gtdb/eval199k-seed/groups199k.csv', 'hf://datasets/risenfromashes/qraft-experiments@2ec102aeb9b7bb5615c175f071e2b6d334a88ade/runs/bdal-2026-10-23-chain-scores-and-seeded-gtdb/eval199k-seed/gtdb199k-groups.csv', 'hf://datasets/risenfromashes/qraft-experiments@2ec102aeb9b7bb5615c175f071e2b6d334a88ade/runs/bdal-2026-10-23-chain-scores-and-seeded-gtdb/eval199k-seed/gtdb199k-summary.csv', 'hf://datasets/risenfromashes/qraft-experiments@2ec102aeb9b7bb5615c175f071e2b6d334a88ade/runs/bdal-2026-10-23-chain-scores-and-seeded-gtdb/eval199k-seed/splits199k.csv', 'hf://datasets/risenfromashes/qraft-experiments@2ec102aeb9b7bb5615c175f071e2b6d334a88ade/runs/local-2026-10-22-chain-eval/eval16k/groups16k.csv', 'hf://datasets/risenfromashes/qraft-experiments@2ec102aeb9b7bb5615c175f071e2b6d334a88ade/runs/local-2026-10-22-chain-eval/eval16k/gtdb16k-groups.csv', 'hf://datasets/risenfromashes/qraft-experiments@2ec102aeb9b7bb5615c175f071e2b6d334a88ade/runs/local-2026-10-22-chain-eval/eval16k/gtdb16k-summary.csv', 'hf://datasets/risenfromashes/qraft-experiments@2ec102aeb9b7bb5615c175f071e2b6d334a88ade/runs/local-2026-10-22-chain-eval/eval16k/splits16k.csv', 'hf://datasets/risenfromashes/qraft-experiments@2ec102aeb9b7bb5615c175f071e2b6d334a88ade/runs/local-2026-10-22-chain-eval/eval199k-default/groups199k.csv', 'hf://datasets/risenfromashes/qraft-experiments@2ec102aeb9b7bb5615c175f071e2b6d334a88ade/runs/local-2026-10-22-chain-eval/eval199k-default/gtdb199k-groups.csv', 'hf://datasets/risenfromashes/qraft-experiments@2ec102aeb9b7bb5615c175f071e2b6d334a88ade/runs/local-2026-10-22-chain-eval/eval199k-default/gtdb199k-summary.csv', 'hf://datasets/risenfromashes/qraft-experiments@2ec102aeb9b7bb5615c175f071e2b6d334a88ade/runs/local-2026-10-22-chain-eval/eval199k-default/splits199k.csv', 'hf://datasets/risenfromashes/qraft-experiments@2ec102aeb9b7bb5615c175f071e2b6d334a88ade/runs/local-2026-10-22-chain-eval/eval199k-extended/groups199k.csv', 'hf://datasets/risenfromashes/qraft-experiments@2ec102aeb9b7bb5615c175f071e2b6d334a88ade/runs/local-2026-10-22-chain-eval/eval199k-extended/gtdb199k-groups.csv', 'hf://datasets/risenfromashes/qraft-experiments@2ec102aeb9b7bb5615c175f071e2b6d334a88ade/runs/local-2026-10-22-chain-eval/eval199k-extended/gtdb199k-summary.csv', 'hf://datasets/risenfromashes/qraft-experiments@2ec102aeb9b7bb5615c175f071e2b6d334a88ade/runs/local-2026-10-22-chain-eval/eval199k-extended/splits199k.csv', 'hf://datasets/risenfromashes/qraft-experiments@2ec102aeb9b7bb5615c175f071e2b6d334a88ade/runs/local-2026-10-22-chain-eval/eval16k/groups16k.csv', 'hf://datasets/risenfromashes/qraft-experiments@2ec102aeb9b7bb5615c175f071e2b6d334a88ade/runs/local-2026-10-22-chain-eval/eval16k/gtdb16k-groups.csv', 'hf://datasets/risenfromashes/qraft-experiments@2ec102aeb9b7bb5615c175f071e2b6d334a88ade/runs/local-2026-10-22-chain-eval/eval16k/gtdb16k-summary.csv', 'hf://datasets/risenfromashes/qraft-experiments@2ec102aeb9b7bb5615c175f071e2b6d334a88ade/runs/local-2026-10-22-chain-eval/eval16k/splits16k.csv', 'hf://datasets/risenfromashes/qraft-experiments@2ec102aeb9b7bb5615c175f071e2b6d334a88ade/runs/local-2026-10-22-chain-eval/eval199k-default/groups199k.csv', 'hf://datasets/risenfromashes/qraft-experiments@2ec102aeb9b7bb5615c175f071e2b6d334a88ade/runs/local-2026-10-22-chain-eval/eval199k-default/gtdb199k-groups.csv', 'hf://datasets/risenfromashes/qraft-experiments@2ec102aeb9b7bb5615c175f071e2b6d334a88ade/runs/local-2026-10-22-chain-eval/eval199k-default/gtdb199k-summary.csv', 'hf://datasets/risenfromashes/qraft-experiments@2ec102aeb9b7bb5615c175f071e2b6d334a88ade/runs/local-2026-10-22-chain-eval/eval199k-default/splits199k.csv', 'hf://datasets/risenfromashes/qraft-experiments@2ec102aeb9b7bb5615c175f071e2b6d334a88ade/runs/local-2026-10-22-chain-eval/eval199k-extended/groups199k.csv', 'hf://datasets/risenfromashes/qraft-experiments@2ec102aeb9b7bb5615c175f071e2b6d334a88ade/runs/local-2026-10-22-chain-eval/eval199k-extended/gtdb199k-groups.csv', 'hf://datasets/risenfromashes/qraft-experiments@2ec102aeb9b7bb5615c175f071e2b6d334a88ade/runs/local-2026-10-22-chain-eval/eval199k-extended/gtdb199k-summary.csv', 'hf://datasets/risenfromashes/qraft-experiments@2ec102aeb9b7bb5615c175f071e2b6d334a88ade/runs/local-2026-10-22-chain-eval/eval199k-extended/splits199k.csv']

Please either edit the data files to have matching columns, or separate them into different configurations (see docs at https://hf.co/docs/hub/datasets-manual-configuration#multiple-configurations)
Traceback:    Traceback (most recent call last):
                File "/usr/local/lib/python3.14/site-packages/datasets/builder.py", line 1856, in _prepare_split_single
                  writer.write_table(table)
                  ~~~~~~~~~~~~~~~~~~^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/arrow_writer.py", line 811, in write_table
                  self._write_table(pa_table, writer_batch_size=writer_batch_size)
                  ~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/arrow_writer.py", line 822, in _write_table
                  pa_table = table_cast(pa_table, self._schema)
                File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2381, in table_cast
                  return cast_table_to_schema(table, schema)
                File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2309, in cast_table_to_schema
                  raise CastError(
                  ...<3 lines>...
                  )
              datasets.table.CastError: Couldn't cast
              rank: string
              group: string
              tips: int64
              uDANCE_mono: int64
              uDANCE_cost: int64
              uDANCE_out: int64
              uDANCE_in: int64
              uDANCE_misplaced: int64
              seed_default_mono: int64
              seed_default_cost: int64
              seed_default_out: int64
              seed_default_in: int64
              seed_default_misplaced: int64
              seed_extended_mono: int64
              seed_extended_cost: int64
              seed_extended_out: int64
              seed_extended_in: int64
              seed_extended_misplaced: int64
              -- schema metadata --
              pandas: '{"index_columns": [{"kind": "range", "name": null, "start": 0, "' + 2531
              to
              {'tree': Value('string'), 'group': Value('string'), 'pure_clades': Value('int64'), 'main_group_taxa': Value('int64'), 'outside_group_taxa': Value('int64'), 'outside_clades': Value('string')}
              because column names don't match
              
              During handling of the above exception, another exception occurred:
              
              Traceback (most recent call last):
                File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 1379, in compute_config_parquet_and_info_response
                  parquet_operations, partial, estimated_dataset_info = stream_convert_to_parquet(
                                                                        ~~~~~~~~~~~~~~~~~~~~~~~~~^
                      builder, max_dataset_size_bytes=max_dataset_size_bytes
                      ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                  )
                  ^
                File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 956, in stream_convert_to_parquet
                  builder._prepare_split(split_generator=splits_generators[split], file_format="parquet")
                  ~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/builder.py", line 1702, in _prepare_split
                  for job_id, done, content in self._prepare_split_single(
                                               ~~~~~~~~~~~~~~~~~~~~~~~~~~^
                      gen_kwargs=gen_kwargs, job_id=job_id, **_prepare_split_args
                      ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                  ):
                  ^
                File "/usr/local/lib/python3.14/site-packages/datasets/builder.py", line 1858, in _prepare_split_single
                  raise DatasetGenerationCastError.from_cast_error(
                  ...<4 lines>...
                  )
              datasets.exceptions.DatasetGenerationCastError: An error occurred while generating the dataset
              
              All the data files must have the same columns, but at some point there are 17 new columns ({'seed_default_mono', 'seed_default_out', 'uDANCE_cost', 'seed_default_cost', 'seed_default_misplaced', 'uDANCE_in', 'seed_extended_in', 'uDANCE_misplaced', 'seed_default_in', 'uDANCE_out', 'seed_extended_cost', 'tips', 'uDANCE_mono', 'seed_extended_mono', 'rank', 'seed_extended_misplaced', 'seed_extended_out'}) and 5 missing columns ({'outside_clades', 'outside_group_taxa', 'main_group_taxa', 'pure_clades', 'tree'}).
              
              This happened while the csv dataset builder was generating data using
              
              hf://datasets/risenfromashes/qraft-experiments/runs/bdal-2026-10-23-chain-scores-and-seeded-gtdb/eval199k-seed/gtdb199k-groups.csv (at revision 2ec102aeb9b7bb5615c175f071e2b6d334a88ade), ['hf://datasets/risenfromashes/qraft-experiments@2ec102aeb9b7bb5615c175f071e2b6d334a88ade/runs/bdal-2026-10-23-chain-scores-and-seeded-gtdb/eval199k-seed/groups199k.csv', 'hf://datasets/risenfromashes/qraft-experiments@2ec102aeb9b7bb5615c175f071e2b6d334a88ade/runs/bdal-2026-10-23-chain-scores-and-seeded-gtdb/eval199k-seed/gtdb199k-groups.csv', 'hf://datasets/risenfromashes/qraft-experiments@2ec102aeb9b7bb5615c175f071e2b6d334a88ade/runs/bdal-2026-10-23-chain-scores-and-seeded-gtdb/eval199k-seed/gtdb199k-summary.csv', 'hf://datasets/risenfromashes/qraft-experiments@2ec102aeb9b7bb5615c175f071e2b6d334a88ade/runs/bdal-2026-10-23-chain-scores-and-seeded-gtdb/eval199k-seed/splits199k.csv', 'hf://datasets/risenfromashes/qraft-experiments@2ec102aeb9b7bb5615c175f071e2b6d334a88ade/runs/local-2026-10-22-chain-eval/eval16k/groups16k.csv', 'hf://datasets/risenfromashes/qraft-experiments@2ec102aeb9b7bb5615c175f071e2b6d334a88ade/runs/local-2026-10-22-chain-eval/eval16k/gtdb16k-groups.csv', 'hf://datasets/risenfromashes/qraft-experiments@2ec102aeb9b7bb5615c175f071e2b6d334a88ade/runs/local-2026-10-22-chain-eval/eval16k/gtdb16k-summary.csv', 'hf://datasets/risenfromashes/qraft-experiments@2ec102aeb9b7bb5615c175f071e2b6d334a88ade/runs/local-2026-10-22-chain-eval/eval16k/splits16k.csv', 'hf://datasets/risenfromashes/qraft-experiments@2ec102aeb9b7bb5615c175f071e2b6d334a88ade/runs/local-2026-10-22-chain-eval/eval199k-default/groups199k.csv', 'hf://datasets/risenfromashes/qraft-experiments@2ec102aeb9b7bb5615c175f071e2b6d334a88ade/runs/local-2026-10-22-chain-eval/eval199k-default/gtdb199k-groups.csv', 'hf://datasets/risenfromashes/qraft-experiments@2ec102aeb9b7bb5615c175f071e2b6d334a88ade/runs/local-2026-10-22-chain-eval/eval199k-default/gtdb199k-summary.csv', 'hf://datasets/risenfromashes/qraft-experiments@2ec102aeb9b7bb5615c175f071e2b6d334a88ade/runs/local-2026-10-22-chain-eval/eval199k-default/splits199k.csv', 'hf://datasets/risenfromashes/qraft-experiments@2ec102aeb9b7bb5615c175f071e2b6d334a88ade/runs/local-2026-10-22-chain-eval/eval199k-extended/groups199k.csv', 'hf://datasets/risenfromashes/qraft-experiments@2ec102aeb9b7bb5615c175f071e2b6d334a88ade/runs/local-2026-10-22-chain-eval/eval199k-extended/gtdb199k-groups.csv', 'hf://datasets/risenfromashes/qraft-experiments@2ec102aeb9b7bb5615c175f071e2b6d334a88ade/runs/local-2026-10-22-chain-eval/eval199k-extended/gtdb199k-summary.csv', 'hf://datasets/risenfromashes/qraft-experiments@2ec102aeb9b7bb5615c175f071e2b6d334a88ade/runs/local-2026-10-22-chain-eval/eval199k-extended/splits199k.csv', 'hf://datasets/risenfromashes/qraft-experiments@2ec102aeb9b7bb5615c175f071e2b6d334a88ade/runs/local-2026-10-22-chain-eval/eval16k/groups16k.csv', 'hf://datasets/risenfromashes/qraft-experiments@2ec102aeb9b7bb5615c175f071e2b6d334a88ade/runs/local-2026-10-22-chain-eval/eval16k/gtdb16k-groups.csv', 'hf://datasets/risenfromashes/qraft-experiments@2ec102aeb9b7bb5615c175f071e2b6d334a88ade/runs/local-2026-10-22-chain-eval/eval16k/gtdb16k-summary.csv', 'hf://datasets/risenfromashes/qraft-experiments@2ec102aeb9b7bb5615c175f071e2b6d334a88ade/runs/local-2026-10-22-chain-eval/eval16k/splits16k.csv', 'hf://datasets/risenfromashes/qraft-experiments@2ec102aeb9b7bb5615c175f071e2b6d334a88ade/runs/local-2026-10-22-chain-eval/eval199k-default/groups199k.csv', 'hf://datasets/risenfromashes/qraft-experiments@2ec102aeb9b7bb5615c175f071e2b6d334a88ade/runs/local-2026-10-22-chain-eval/eval199k-default/gtdb199k-groups.csv', 'hf://datasets/risenfromashes/qraft-experiments@2ec102aeb9b7bb5615c175f071e2b6d334a88ade/runs/local-2026-10-22-chain-eval/eval199k-default/gtdb199k-summary.csv', 'hf://datasets/risenfromashes/qraft-experiments@2ec102aeb9b7bb5615c175f071e2b6d334a88ade/runs/local-2026-10-22-chain-eval/eval199k-default/splits199k.csv', 'hf://datasets/risenfromashes/qraft-experiments@2ec102aeb9b7bb5615c175f071e2b6d334a88ade/runs/local-2026-10-22-chain-eval/eval199k-extended/groups199k.csv', 'hf://datasets/risenfromashes/qraft-experiments@2ec102aeb9b7bb5615c175f071e2b6d334a88ade/runs/local-2026-10-22-chain-eval/eval199k-extended/gtdb199k-groups.csv', 'hf://datasets/risenfromashes/qraft-experiments@2ec102aeb9b7bb5615c175f071e2b6d334a88ade/runs/local-2026-10-22-chain-eval/eval199k-extended/gtdb199k-summary.csv', 'hf://datasets/risenfromashes/qraft-experiments@2ec102aeb9b7bb5615c175f071e2b6d334a88ade/runs/local-2026-10-22-chain-eval/eval199k-extended/splits199k.csv']
              
              Please either edit the data files to have matching columns, or separate them into different configurations (see docs at https://hf.co/docs/hub/datasets-manual-configuration#multiple-configurations)

Need help to make the dataset viewer work? Make sure to review how to configure the dataset viewer, and open a discussion for direct support.

tree
string
group
string
pure_clades
int64
main_group_taxa
int64
outside_group_taxa
int64
outside_clades
string
seed_default
gtdb_domain=Archaea
58
1,446
2,282
426/326 376/302 369/301 225/188 272/159 131/98 105/95 121/92 97/80 67/56 93/46 42/37 41/33 62/32 51/30 67/27 42/26 28/25 35/23 25/23 27/19 24/18 24/15 16/15 19/14 22/13 18/12 13/12 13/12 20/11 13/10 12/10 13/9 10/9 19/8 25/7 14/7 12/7 9/7 7/7 10/6 9/6 7/6 11/5 9/5 5/5 10/4 7/4 7/4 8/3 5/3 4/3 10/2 2/2 5/1 3/1 1/1
seed_default
gtdb_phylum=Patescibacteria
54
365
2,326
480/274 241/198 307/194 208/163 274/150 139/113 175/107 147/107 154/103 174/102 119/96 126/86 130/83 117/76 93/64 62/44 44/36 55/33 43/27 36/22 34/18 23/18 22/18 18/18 22/17 23/16 21/16 16/14 20/12 15/12 17/11 15/9 15/8 10/7 9/7 16/6 15/5 13/5 7/5 10/4 4/4 6/3 6/3 4/3 5/1 5/1 3/1 2/1 2/1 2/1 2/1 2/1 1/1
seed_default
gtdb_phylum=Spirochaetota
3
1,246
52
56/46 9/6
seed_default
gtdb_phylum=Firmicutes_C
1
626
0
null
seed_default
gtdb_phylum=Cyanobacteria
4
1,718
174
248/156 26/11 8/7
seed_extended
gtdb_domain=Archaea
1
3,728
0
null
seed_extended
gtdb_phylum=Patescibacteria
1
2,691
0
null
seed_extended
gtdb_phylum=Spirochaetota
2
784
514
524/514
seed_extended
gtdb_phylum=Firmicutes_C
1
626
0
null
seed_extended
gtdb_phylum=Cyanobacteria
1
1,892
0
null
null
Bacteria
null
null
null
null
null
Archaea
null
null
null
null
null
Proteobacteria
null
null
null
null
null
Firmicutes
null
null
null
null
null
Actinobacteriota
null
null
null
null
null
Bacteroidota
null
null
null
null
null
Firmicutes_A
null
null
null
null
null
Campylobacterota
null
null
null
null
null
Patescibacteria
null
null
null
null
null
Cyanobacteria
null
null
null
null
null
Spirochaetota
null
null
null
null
null
Chloroflexota
null
null
null
null
null
Verrucomicrobiota
null
null
null
null
null
Halobacteriota
null
null
null
null
null
Thermoplasmatota
null
null
null
null
null
Planctomycetota
null
null
null
null
null
Acidobacteriota
null
null
null
null
null
Thermoproteota
null
null
null
null
null
Desulfobacterota
null
null
null
null
null
Firmicutes_C
null
null
null
null
null
Firmicutes_B
null
null
null
null
null
Chlamydiota
null
null
null
null
null
Methanobacteriota
null
null
null
null
null
Myxococcota
null
null
null
null
null
Gemmatimonadota
null
null
null
null
null
Nitrospirota
null
null
null
null
null
Fusobacteriota
null
null
null
null
null
Desulfobacterota_I
null
null
null
null
null
Nanoarchaeota
null
null
null
null
null
Marinisomatota
null
null
null
null
null
Deinococcota
null
null
null
null
null
Firmicutes_G
null
null
null
null
null
Bdellovibrionota
null
null
null
null
null
Thermotogota
null
null
null
null
null
Dormibacterota
null
null
null
null
null
Elusimicrobiota
null
null
null
null
null
Omnitrophota
null
null
null
null
null
Firmicutes_D
null
null
null
null
null
Fibrobacterota
null
null
null
null
null
Synergistota
null
null
null
null
null
Armatimonadota
null
null
null
null
null
Methanobacteriota_B
null
null
null
null
null
Firmicutes_F
null
null
null
null
null
Eremiobacterota
null
null
null
null
null
Firmicutes_E
null
null
null
null
null
Cloacimonadota
null
null
null
null
null
SAR324
null
null
null
null
null
Methylomirabilota
null
null
null
null
null
WOR-3
null
null
null
null
null
Aquificota
null
null
null
null
null
Micrarchaeota
null
null
null
null
null
Margulisbacteria
null
null
null
null
null
Latescibacterota
null
null
null
null
null
Asgardarchaeota
null
null
null
null
null
Desulfobacterota_B
null
null
null
null
null
Myxococcota_A
null
null
null
null
null
Desulfobacterota_D
null
null
null
null
null
Nitrospinota
null
null
null
null
null
Poribacteria
null
null
null
null
null
Aenigmatarchaeota
null
null
null
null
null
Atribacterota
null
null
null
null
null
Hydrogenedentota
null
null
null
null
null
Dependentiae
null
null
null
null
null
Methanobacteriota_A
null
null
null
null
null
Desulfobacterota_G
null
null
null
null
null
Altiarchaeota
null
null
null
null
null
Zixibacteria
null
null
null
null
null
Iainarchaeota
null
null
null
null
null
Desulfobacterota_E
null
null
null
null
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UBA10199
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Deferribacterota
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KSB1
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Fermentibacterota
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Riflebacteria
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Bipolaricaulota
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Hadarchaeota
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DTU030
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Edwardsbacteria
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Eisenbacteria
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Nanohaloarchaeota
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Dictyoglomota
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Calditrichota
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Huberarchaeota
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Sumerlaeota
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Bdellovibrionota_C
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Desantisbacteria
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CG2-30-70-394
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Firmicutes_H
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End of preview.

QRAFT experiments

Data and results of the experiments on QRAFT, a species-tree method based on quartets, and of its comparison with ASTRAL-X and STELAR-X: simulated data sets, the runs of the three methods, the ablation of QRAFT's pipeline, timings measured with a processor socket to themselves, and runs on biological data. This repository was named risenfromashes/simphy-1M until 2026-10-09; logs written before then still use that name.

Folder Contents
raw/, provenance/ SimPhy data sets for the sweep to 1,000,000 taxa and gene trees, 5 replicates, and how they were simulated
vary-ils/ data sets at four levels of incomplete lineage sorting (ILS), and QRAFT's runs on them
runs/vietnam-2026-09-26/ the first QRAFT sweep, on the ASTRAL-X benchmark's data sets, up to 300,000 taxa and gene trees
runs/texas-2026-10-02/ the run box of the five-replicate sweep: QRAFT, ASTRAL-X and STELAR-X runs
clean-timing/2026-10-09/ timings measured with a socket to themselves
ablation/ the ablations of QRAFT's pipeline
angio/, avian-48/, avian-363/, udance/ runs on biological data

QRAFT is qraft-38d32f1, the static Release build of commit 38d32f1, unless a measurement build is named. Data set names follow the benchmark harness phylo-bench: t_<taxa>_g_<genes>_sb_<speciation rate>_spmin_<min Ne>_spmax_<max Ne>, with replicates R1–R5.

raw/: the sweep to 1,000,000 taxa and gene trees

Simulated species trees and gene trees for two sweeps used to benchmark species-tree inference at scale:

  • taxa sweep: 100,000 to 1,000,000 taxa in steps of 100,000, each with 1,000 gene trees;
  • gene sweep: 100,000 to 1,000,000 gene trees in steps of 100,000, each with 1,000 taxa.

Every size has 5 replicates (R1–R5), each a species tree (s_tree.trees) and its gene trees (all_gt.tre, one Newick tree per line).

Model and command

The data were generated by the benchmark harness phylo-bench (commit 7aa3c87e49bfa6b6032ff81759b21740327385fb), unchanged, with the simphy-x engine in compat mode, which produces trees byte-identical to SimPhy 1.0.2. For every size T taxa × G genes:

./scripts/sim.sh -rs 5 --simphy-data-dir DATA --seed 42 --engine simphy-x-compat \
    --sim-threads all -t T -g G --sb 0.000001 --spmin 100000 --spmax 200000

which runs (see provenance/simphy-commands.txt for each data set's recorded command line):

simphy-x -XM compat -sb f:0.000001 -ld f:0 -lb f:0 -lt f:0 -rs 5 -rl f:G -rg 1 -o OUT \
    -sp u:100000,200000 -su ln:-17.27461,0.6931472 -sg f:1 -sl f:T -st ln:16.2,1 \
    -om 1 -v 2 -od 1 -op 1 -oc 1 -on 1 -cs 42

then concatenates each replicate's gene trees into all_gt.tre (dropping SimPhy's _0_0 label suffixes) with the harness's concat_gene_trees.py and reorganize_trees.py. Speciation rate 1e-6, no duplication, loss or transfer, effective population size uniform in [100,000, 200,000], species-tree height lognormal(16.2, 1), seed 42.

The 100k, 200k and 300k data sets of both sweeps are identical to those of the ASTRAL-X benchmark (imAniksahA/blab, ph/d/simulated/astralx-datasets/raw/): every tree file has the same CRC-32 and size. Only SimPhy's .command, .params and .db files differ, since they record the binary's and the output's paths.

Layout

raw/t_T_g_G_sb_0.000001_spmin_100000_spmax_200000.zip           (T × G ≤ 0.6e9)
raw/t_T_g_G_sb_0.000001_spmin_100000_spmax_200000.R1-R3.zip     (T × G ≥ 0.7e9: two parts,
raw/t_T_g_G_sb_0.000001_spmin_100000_spmax_200000.R4-R5.zip      under the 50 GB file limit)
provenance/

Each zip holds the data set's directory with SimPhy's .command, .params and .db files and its replicates, R<n>/all_gt.tre and R<n>/s_tree.trees. Unzip both parts of a split data set into the same directory to get R1–R5:

unzip t_1000000_g_1000_sb_0.000001_spmin_100000_spmax_200000.R1-R3.zip
unzip -o t_1000000_g_1000_sb_0.000001_spmin_100000_spmax_200000.R4-R5.zip

Check the trees against provenance/sha256-trees.txt (sha256sum -c after cd into the extraction directory).

Provenance

  • sim-commands.log: every command run (sim.sh and zip), verbatim, with start and end time, working directory and exit status (JSON lines).
  • simphy-commands.txt: the SimPhy command line each data set recorded.
  • sha256-trees.txt, sha256-zips.txt: sha256 of every tree file and every zip (for a zip packed more than once, the last line is the uploaded one).
  • environment-helper*.txt: the machines, the engine version and the sha256 of the harness scripts (identical on every machine).
  • NOTES.txt: what happened during the run, in order.
  • sim1m.py (driver), zip-parallel.py (multi-core zip -r), reorg_guard.py (memory guard), cleanup_restart.sh, pack_manual.py: the scripts that ran around the harness.
  • logs/helper<n>-*.tgz: each machine's harness simulation logs and driver logs.

The data sets were simulated on rented vast.ai machines, with the same harness scripts (same sha256) on each:

Machine Data sets
helper 1: Ryzen 9 9950X, Minnesota taxa 100k, 200k, 300k, 500k–900k; genes 100k, 800k
helper 2: Ryzen 9 9950X, Japan none (its disk stalled; abandoned)
helper 3: EPYC 7B13, Nebraska taxa 1M; genes 900k, 1M
helper 4: Ryzen 9 9950X, Utah taxa 400k; genes 200k–700k

Helper 1 rebooted twice, and helper 3's filesystem stalled while deleting SimPhy's per-locus files, after its three data sets were complete. Data sets in progress at a failure were simulated again from the start; completed ones were kept. NOTES.txt records every step. Zips were made with Info-ZIP 3.0 or, later, with zip-parallel.py: same layout and deflate format, slightly different bytes; the tree files are identical either way.

vary-ils/: four levels of ILS

The varying-ILS benchmark of phylo-bench: 40 SimPhy data sets with five replicates each. They are 1,000 to 40,000 taxa with 1,000 gene trees, and 1,000 taxa with 5,000 to 50,000 gene trees, each at four ILS levels set by the largest effective population size: spmax 150,000, 200,000, 250,000 and 300,000 for ILS-L1 to ILS-L4 (spmin 100,000, speciation rate 1e-6). ILS-L2 has the sweep's settings.

  • raw/<data set>.zip: the data sets, laid out as in the top-level raw/.
  • qraft_outputs/<data set>/: QRAFT with 64 threads on every replicate.
  • stat-vary-ils-qraft.csv and stat-vary-ils-qraft-summary.csv: one row per run, and the mean and standard deviation per data set.
  • run-vary-ils.log, ils.log, collect.log: the harness's logs.

116 of these 200 runs shared their socket with ablation jobs, so their times are too long: 30,000 taxa at ILS-L1 R5 and at ILS-L2 to L4, all of 40,000 taxa, and 1,000 taxa with 5,000 to 50,000 genes. clean-timing/2026-10-09/vils-clean.csv holds their clean times. The trees, and so the RF rates, do not depend on the load.

runs/vietnam-2026-09-26/: the first sweep, on the ASTRAL-X benchmark's data

QRAFT with 32 threads on an AMD EPYC 7742 (the processor of the ASTRAL-X and STELAR-X papers), from 2026-09-26 to 2026-09-28. It covers 126 runs: 1,000 to 300,000 taxa with 1,000 gene trees, and 2,500 to 300,000 gene trees with 1,000 taxa. Each run is on a replicate that has a reference ASTRAL-X run, and the trees are identical to the ASTRAL-X benchmark's. This includes the papers' comparisons with ASTRAL-MP and ASTER up to 10,000 taxa and 50,000 gene trees. results/qraft-sweep.csv lists every run, with its RF rate, time and peak memory, and results/qraft-vs-reference.txt compares QRAFT with ASTRAL-X and STELAR-X. The folder's README describes the per-run outputs, and the calibration of this machine against the papers' times.

runs/texas-2026-10-02/: the five-replicate sweep

The vast.ai machine the sweep ran on from 2026-10-02 to 2026-10-09: two 32-core AMD EPYC processors, 409 GiB of memory and an RTX 4090. Every run had one socket to itself, with 64 threads. QRAFT has up to five replicates per size, and three for each of 700,000 to 1,000,000 taxa. ASTRAL-X and STELAR-X ran as references, mostly one replicate per size; results/runs.csv lists every run and its status. Sizes not in raw/, the finer steps up to 300,000, come from the ASTRAL-X benchmark's data (imAniksahA/blab).

  • results/runs.csv and results/jobs.jsonl: every run, with its status, wall time, peak memory and RF rate. results/runbox.log is the driver's log, results/logs/ holds the harness logs, results/environment.txt describes the machine and results/thermal.log its clocks and temperatures.
  • results/r275k/: the third 275,000-taxon replicate, R2, run after the driver had finished.
  • results/highils/, results/bio/, results/udance/: high-ILS replicates rerun without a time limit, and the biological and uDance runs of this machine.
  • results/CONTAMINATED.txt: one ASTRAL-X run whose time is not usable.
  • outputs/: the harness's outputs (qraft_outputs/, astralx_outputs/, stelarx_outputs/), with trees, statistics and command records.
  • health/: the GPU and QRAFT checks run when the machine was rented.

clean-timing/2026-10-09/: clean timings

Runs timed with a socket to themselves and nothing else on it, by clean_runner.sh (one runner per socket) from the queue that build_queue.py wrote. queue/done/ holds each item's script and log.

  • vils-clean.csv (the harness's columns, no header): the 116 varying-ILS runs above, rerun alone.
  • timing.csv (group,dataset,rep,variant,wall_s,maxrss_kb,exit):
    • the nine pipeline variants on ASTRAL-II, 1,000-taxon, ASTRAL-MP 10k, ASTRAL-III, 1KP, B10K exons and intergenic, the angiosperms and avian-48. The variants are qraft, no_nni, mre, mre_nni, mre_nni_div, balanced, no_test, regraft_norm and nni_to_end. They ran with the measurement build qraft-nni-test3, whose default output equals the release's.
    • true against estimated gene trees at 500, 1,000 and 10,000 taxa;
    • the biological data sets, three runs each.
  • discordance.csv (no header; data set, replicate, taxa, genes, genes sampled, GT-ST %, gene-tree pairs sampled, GT-GT %): the discordance of gene trees with the species tree (GT-ST) and between pairs of gene trees (GT-GT), computed by discord.py.
  • results/: the trees and the time and memory record of every run.

Five varying-ILS replicates took 736 to 7,516 s where their siblings took about 100 to 180 s: 30,000 taxa at L1 R5, L3 R4 and L4 R3, and 40,000 taxa at L1 R5 and L2 R5. Each has far more discordant gene trees than its siblings.

ablation/: the ablations of QRAFT's pipeline

  • 2026-10-08/: the full ablation, 1,706 jobs with none failed. ablation-all.csv has one row per tree, 45,452 rows with RF and the tqDist quartet score. trees/<family>.tar.gz holds each family's trees. The families are the 37-, 48-, 100-, 200-, 500- and 1,000-taxon sets, ASTRAL-II, ASTRAL-III 2000, ASTRAL-MP 10k, 1KP and the varying-ILS data sets.
    • Bases: the default pipeline, MRE only, balanced divide, no bipartition test, normalized regraft, 4 proposals with 2 anchor pairs, and MRE, then NNI, then divide.
    • Each base runs without NNI and with three NNI modes: the release's (at most 20 moves, support gate), batched with no move cap, and batched with no cap but with the gate.
    • The varying-ILS sets have only the default and MRE bases.
    • The times are not clean; clean-timing/ has clean ones.
  • 2026-09-30-texas/ and 2026-10-01-local/: the first ablation, on an earlier machine, and the quartet scores computed locally afterwards. Their READMEs describe them.

Biological data

QRAFT with 64 threads on four data sets. Each inputs.tsv names the input files, which are not copied here, and each biological-dataset.command says where they come from.

  • angio/: the angiosperms (1KP), 9,524 taxa, 353 gene trees.
  • avian-48/: 48 birds, 14,446 gene trees.
  • avian-363/: B10K intergenic, 363 birds, 63,430 gene trees.
    • It also holds an ASTRAL-X run and two QRAFT trees with uncapped NNI.
    • qraft-vs-astralx.txt compares their time, memory, RF and quartet scores.
  • udance/qraft_outputs/batch-nni/: QRAFT end to end on the pruned uDance gene trees (380 genes, 199,330 taxa), with batched NNI and no move cap, run with the measurement build. README.txt gives the settings and nni.trace every accepted move.
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