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The dataset generation failed
Error code: DatasetGenerationError
Exception: CastError
Message: Couldn't cast
all_missing_candidates: int64
beta: double
budgets: list<item: int64>
child 0, item: int64
effect_predictor_reproduction_max_abs_error: double
evaluation_split: string
fusion: string
fusion_selected_before_G_check: bool
high_value_quantile: double
lock_path: string
lock_sha256: string
method_of_record: string
must_not_be_called: string
paired: struct<CLEAN_BASE_minus_LEGACY_M_SET: struct<bootstrap: struct<clusters: int64, mean: struct<ci95: l (... 2504 chars omitted)
child 0, CLEAN_BASE_minus_LEGACY_M_SET: struct<bootstrap: struct<clusters: int64, mean: struct<ci95: list<item: double>, fraction_of_replica (... 290 chars omitted)
child 0, bootstrap: struct<clusters: int64, mean: struct<ci95: list<item: double>, fraction_of_replicates_above_zero: do (... 153 chars omitted)
child 0, clusters: int64
child 1, mean: struct<ci95: list<item: double>, fraction_of_replicates_above_zero: double, point: double>
child 0, ci95: list<item: double>
child 0, item: double
child 1, fraction_of_replicates_above_zero: double
child 2, point: double
child 2, median: struct<ci95: list<item: double>, fraction_of_replicates_above_zero: double, point: double>
child 0, ci95: list<item: double>
child 0, item: double
child 1, fraction_of_replicates_above_zero: double
child 2, point: double
child 3, replicates: int64
child 4, see
...
_a_tensor: bool
history: list<item: struct<check_response_accessed: bool, epoch: int64, epoch_seconds: double, gradient_norm: (... 205 chars omitted)
child 0, item: struct<check_response_accessed: bool, epoch: int64, epoch_seconds: double, gradient_norm: double, ha (... 193 chars omitted)
child 0, check_response_accessed: bool
child 1, epoch: int64
child 2, epoch_seconds: double
child 3, gradient_norm: double
child 4, hard_negative_fraction: double
child 5, logit_scale: double
child 6, loss: double
child 7, objective: string
child 8, pair_count: double
child 9, pair_loss: double
child 10, peak_memory_mb: double
child 11, phase: string
child 12, set_loss: double
child 13, steps: int64
parameters: int64
text_status: string
checkpoints: struct<10: struct<path: string, sha256: string>, 6: struct<path: string, sha256: string>, 7: struct< (... 112 chars omitted)
child 0, 10: struct<path: string, sha256: string>
child 0, path: string
child 1, sha256: string
child 1, 6: struct<path: string, sha256: string>
child 0, path: string
child 1, sha256: string
child 2, 7: struct<path: string, sha256: string>
child 0, path: string
child 1, sha256: string
child 3, 8: struct<path: string, sha256: string>
child 0, path: string
child 1, sha256: string
child 4, 9: struct<path: string, sha256: string>
child 0, path: string
child 1, sha256: string
to
{'all_missing_rule': Value('string'), 'checkpoints': {'10': {'path': Value('string'), 'sha256': Value('string')}, '6': {'path': Value('string'), 'sha256': Value('string')}, '7': {'path': Value('string'), 'sha256': Value('string')}, '8': {'path': Value('string'), 'sha256': Value('string')}, '9': {'path': Value('string'), 'sha256': Value('string')}}, 'history': List({'check_response_accessed': Value('bool'), 'epoch': Value('int64'), 'epoch_seconds': Value('float64'), 'gradient_norm': Value('float64'), 'hard_negative_fraction': Value('float64'), 'logit_scale': Value('float64'), 'loss': Value('float64'), 'objective': Value('string'), 'pair_count': Value('float64'), 'pair_loss': Value('float64'), 'peak_memory_mb': Value('float64'), 'phase': Value('string'), 'set_loss': Value('float64'), 'steps': Value('int64')}), 'method_of_record': Value('string'), 'modalities': List(Value('string')), 'modality_dimensions': {'MAPKG': Value('int64'), 'STRING': Value('int64')}, 'parameters': Value('int64'), 'provenance': {'assets': {'cache': {'path': Value('string'), 'sha256': Value('string')}, 'cache_ids': {'path': Value('string'), 'sha256': Value('string')}, 'cache_manifest': {'path': Value('string'), 'sha256': Value('string')}, 'candidate_knowledge': {'path': Value('string'), 'sha256': Value('string')}, 'candidate_knowledge_freeze': {'path': Value('string'), 'sha256': Value('string')}, 'freeze': {'path': Value('string'), 'sha256': Value('string')}, 'goal_states': {'path': Value('string'), 'sha25
...
lue('string')}, 'source_goal_manifest': {'path': Value('string'), 'sha256': Value('string')}, 'source_states': {'path': Value('string'), 'sha256': Value('string')}, 'string': {'path': Value('string'), 'sha256': Value('string')}}, 'cached_scores': {'FUNCTION_G_select': {'path': Value('string'), 'sha256': Value('string')}, 'M_SET_G_fit': {'path': Value('string'), 'sha256': Value('string')}, 'M_SET_G_select': {'path': Value('string'), 'sha256': Value('string')}, 'PAR_REAL_G_select': {'path': Value('string'), 'sha256': Value('string')}, 'PAR_SHUFFLED_G_select': {'path': Value('string'), 'sha256': Value('string')}}, 'checkpoints': {'M_PAIR': {'detail': {'epoch': Value('int64'), 'path': Value('string'), 'rule': Value('string')}, 'path': Value('string'), 'sha256': Value('string')}, 'M_SET': {'detail': {'epoch': Value('int64'), 'path': Value('string'), 'rule': Value('string')}, 'path': Value('string'), 'sha256': Value('string')}}}, 'read_G_check': Value('bool'), 'recipe': {'amp_bfloat16': Value('bool'), 'base_epochs': List(Value('int64')), 'batch_size': Value('int64'), 'branch_epochs': List(Value('int64')), 'gradient_clip_norm': Value('float64'), 'lr': Value('float64'), 'objective_schedule': Value('string'), 'seed': Value('int64'), 'source': Value('string'), 'weight_decay': Value('float64')}, 'runtime': {'python': Value('string'), 'timestamp': Value('string'), 'torch': Value('string')}, 'schema': Value('string'), 'text_reached_a_tensor': Value('bool'), 'text_status': Value('string')}
because column names don't match
Traceback: Traceback (most recent call last):
File "/usr/local/lib/python3.14/site-packages/datasets/builder.py", line 1827, in _prepare_split_single
for key, table in generator:
^^^^^^^^^
File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 613, in wrapped
for item in generator(*args, **kwargs):
~~~~~~~~~^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/json/json.py", line 343, in _generate_tables
self._cast_table(pa_table, json_field_paths=json_field_paths),
~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/json/json.py", line 132, in _cast_table
pa_table = table_cast(pa_table, self.info.features.arrow_schema)
File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2378, in table_cast
return cast_table_to_schema(table, schema)
File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2306, in cast_table_to_schema
raise CastError(
...<3 lines>...
)
datasets.table.CastError: Couldn't cast
all_missing_candidates: int64
beta: double
budgets: list<item: int64>
child 0, item: int64
effect_predictor_reproduction_max_abs_error: double
evaluation_split: string
fusion: string
fusion_selected_before_G_check: bool
high_value_quantile: double
lock_path: string
lock_sha256: string
method_of_record: string
must_not_be_called: string
paired: struct<CLEAN_BASE_minus_LEGACY_M_SET: struct<bootstrap: struct<clusters: int64, mean: struct<ci95: l (... 2504 chars omitted)
child 0, CLEAN_BASE_minus_LEGACY_M_SET: struct<bootstrap: struct<clusters: int64, mean: struct<ci95: list<item: double>, fraction_of_replica (... 290 chars omitted)
child 0, bootstrap: struct<clusters: int64, mean: struct<ci95: list<item: double>, fraction_of_replicates_above_zero: do (... 153 chars omitted)
child 0, clusters: int64
child 1, mean: struct<ci95: list<item: double>, fraction_of_replicates_above_zero: double, point: double>
child 0, ci95: list<item: double>
child 0, item: double
child 1, fraction_of_replicates_above_zero: double
child 2, point: double
child 2, median: struct<ci95: list<item: double>, fraction_of_replicates_above_zero: double, point: double>
child 0, ci95: list<item: double>
child 0, item: double
child 1, fraction_of_replicates_above_zero: double
child 2, point: double
child 3, replicates: int64
child 4, see
...
_a_tensor: bool
history: list<item: struct<check_response_accessed: bool, epoch: int64, epoch_seconds: double, gradient_norm: (... 205 chars omitted)
child 0, item: struct<check_response_accessed: bool, epoch: int64, epoch_seconds: double, gradient_norm: double, ha (... 193 chars omitted)
child 0, check_response_accessed: bool
child 1, epoch: int64
child 2, epoch_seconds: double
child 3, gradient_norm: double
child 4, hard_negative_fraction: double
child 5, logit_scale: double
child 6, loss: double
child 7, objective: string
child 8, pair_count: double
child 9, pair_loss: double
child 10, peak_memory_mb: double
child 11, phase: string
child 12, set_loss: double
child 13, steps: int64
parameters: int64
text_status: string
checkpoints: struct<10: struct<path: string, sha256: string>, 6: struct<path: string, sha256: string>, 7: struct< (... 112 chars omitted)
child 0, 10: struct<path: string, sha256: string>
child 0, path: string
child 1, sha256: string
child 1, 6: struct<path: string, sha256: string>
child 0, path: string
child 1, sha256: string
child 2, 7: struct<path: string, sha256: string>
child 0, path: string
child 1, sha256: string
child 3, 8: struct<path: string, sha256: string>
child 0, path: string
child 1, sha256: string
child 4, 9: struct<path: string, sha256: string>
child 0, path: string
child 1, sha256: string
to
{'all_missing_rule': Value('string'), 'checkpoints': {'10': {'path': Value('string'), 'sha256': Value('string')}, '6': {'path': Value('string'), 'sha256': Value('string')}, '7': {'path': Value('string'), 'sha256': Value('string')}, '8': {'path': Value('string'), 'sha256': Value('string')}, '9': {'path': Value('string'), 'sha256': Value('string')}}, 'history': List({'check_response_accessed': Value('bool'), 'epoch': Value('int64'), 'epoch_seconds': Value('float64'), 'gradient_norm': Value('float64'), 'hard_negative_fraction': Value('float64'), 'logit_scale': Value('float64'), 'loss': Value('float64'), 'objective': Value('string'), 'pair_count': Value('float64'), 'pair_loss': Value('float64'), 'peak_memory_mb': Value('float64'), 'phase': Value('string'), 'set_loss': Value('float64'), 'steps': Value('int64')}), 'method_of_record': Value('string'), 'modalities': List(Value('string')), 'modality_dimensions': {'MAPKG': Value('int64'), 'STRING': Value('int64')}, 'parameters': Value('int64'), 'provenance': {'assets': {'cache': {'path': Value('string'), 'sha256': Value('string')}, 'cache_ids': {'path': Value('string'), 'sha256': Value('string')}, 'cache_manifest': {'path': Value('string'), 'sha256': Value('string')}, 'candidate_knowledge': {'path': Value('string'), 'sha256': Value('string')}, 'candidate_knowledge_freeze': {'path': Value('string'), 'sha256': Value('string')}, 'freeze': {'path': Value('string'), 'sha256': Value('string')}, 'goal_states': {'path': Value('string'), 'sha25
...
lue('string')}, 'source_goal_manifest': {'path': Value('string'), 'sha256': Value('string')}, 'source_states': {'path': Value('string'), 'sha256': Value('string')}, 'string': {'path': Value('string'), 'sha256': Value('string')}}, 'cached_scores': {'FUNCTION_G_select': {'path': Value('string'), 'sha256': Value('string')}, 'M_SET_G_fit': {'path': Value('string'), 'sha256': Value('string')}, 'M_SET_G_select': {'path': Value('string'), 'sha256': Value('string')}, 'PAR_REAL_G_select': {'path': Value('string'), 'sha256': Value('string')}, 'PAR_SHUFFLED_G_select': {'path': Value('string'), 'sha256': Value('string')}}, 'checkpoints': {'M_PAIR': {'detail': {'epoch': Value('int64'), 'path': Value('string'), 'rule': Value('string')}, 'path': Value('string'), 'sha256': Value('string')}, 'M_SET': {'detail': {'epoch': Value('int64'), 'path': Value('string'), 'rule': Value('string')}, 'path': Value('string'), 'sha256': Value('string')}}}, 'read_G_check': Value('bool'), 'recipe': {'amp_bfloat16': Value('bool'), 'base_epochs': List(Value('int64')), 'batch_size': Value('int64'), 'branch_epochs': List(Value('int64')), 'gradient_clip_norm': Value('float64'), 'lr': Value('float64'), 'objective_schedule': Value('string'), 'seed': Value('int64'), 'source': Value('string'), 'weight_decay': Value('float64')}, 'runtime': {'python': Value('string'), 'timestamp': Value('string'), 'torch': Value('string')}, 'schema': Value('string'), 'text_reached_a_tensor': Value('bool'), 'text_status': Value('string')}
because column names don't match
The above exception was the direct cause of the following exception:
Traceback (most recent call last):
File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 1369, 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 948, 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 1694, 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 1880, in _prepare_split_single
raise DatasetGenerationError("An error occurred while generating the dataset") from e
datasets.exceptions.DatasetGenerationError: An error occurred while generating the datasetNeed help to make the dataset viewer work? Make sure to review how to configure the dataset viewer, and open a discussion for direct support.
all_missing_rule string | checkpoints dict | history list | method_of_record string | modalities list | modality_dimensions dict | parameters int64 | provenance dict | read_G_check bool | recipe dict | runtime dict | schema string | text_reached_a_tensor bool | text_status string |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
no learnable shared unknown token; a candidate with no legal modality contributes exactly zero to the score for every query, identically in both arms; the present mask is retained | {
"6": {
"path": "UNBUNDLED/epoch_006.pt",
"sha256": "f1f9b48f73bb5a58fbf4f71dd1953f164ebaa8c6900102a8ceb33f1c4c7102cf"
},
"7": {
"path": "UNBUNDLED/epoch_007.pt",
"sha256": "fb39d5e3e26fa44741afe30e535dea94260a67703324c44ca6d5df7931d8ea8f"
},
"8": {
"path": "checkpoints/final_clean_epoch_... | [
{
"check_response_accessed": false,
"epoch": 1,
"epoch_seconds": 19.600495742051862,
"gradient_norm": 0.8203064134117983,
"hard_negative_fraction": 0.2831313935462062,
"logit_scale": 14.250326156616211,
"loss": 0.5789684117875679,
"objective": "PAIR",
"pair_count": 65505.50868486... | CANDIDATE_EFFECT_DISTILLATION_V1 | [
"STRING",
"MAPKG"
] | {
"MAPKG": 1024,
"STRING": 512
} | 30,111,233 | {
"assets": {
"cache": {
"path": "inputs/responses_ab.npy",
"sha256": "bb48bae7f864c66b242a77cef064ff967b418d22bb11416630548f6a6d094a94"
},
"cache_ids": {
"path": "inputs/eligible_ids.npy",
"sha256": "40e104ed924c109d621f15bc5bb3bf899ca89c3f14db046b5aa5ae0c4ce611a7"
},
"cac... | false | {
"amp_bfloat16": true,
"base_epochs": [
1,
5
],
"batch_size": 8,
"branch_epochs": [
6,
10
],
"gradient_clip_norm": 1,
"lr": 0.0001,
"objective_schedule": "PAIR then SET",
"seed": 20260916,
"source": "gene_open_inverse.model.global_objective_knowledge_attribution_v1.train_factorial... | {
"python": "3.11.10",
"timestamp": "2026-09-18T03:39:36.297624+00:00",
"torch": "2.4.1+cu118"
} | VCDESIGN_FINAL_CLEAN_MODEL_V1_TRAIN | false | EXCLUDED_FROM_FINAL_PRIMARY_TRACK |
VCDesign-CED processed data
This repository stores the frozen processed inputs used by the VCDesign-CED paper, plus the selected base-model checkpoint and compact evaluation records. The source single-cell H5AD files are obtained from the original public releases and are not duplicated here.
Contents
| Directory | Contents |
|---|---|
inputs/ |
The 19 frozen inputs referenced by the paper's training configuration, plus source-goal metadata |
inputs/four_context/packs/ |
K562, RPE1, HepG2 and Jurkat processed context packs |
inputs/external_baselines/scores/ |
Frozen external-comparator score matrices for RPE1, HepG2 and Jurkat |
checkpoints/ |
Selected epoch-8 VCDesign-CED base-model checkpoint |
records/ |
Training, selection, evaluation, context-build and comparator verification records |
code/ |
Versioned, anonymous code-package ZIP for local download and review |
DATA_MANIFEST.json |
File sizes, release SHA-256 values and original SHA-256 values where a record was anonymized |
Code package
Download the latest verified code package directly:
VCDesign_ICLR2027_code_v0.2.0.zip
SHA-256: 78c96552727b378ecde418bac9e4f9015739c5e49e4aeac7b57a345c220c40ab
Version 0.2.0 places gene_open_inverse/ directly at the package root and adds
an anonymous, visual README with a complete local copy of Figure 1. The ZIP also
contains the frozen configuration, exact CPU verification dependencies, a
synthetic demo, 162 protocol tests, and its own per-file
CODE_PACKAGE_MANIFEST.sha256. It does not contain large processed inputs,
checkpoints, Git history, or author-account links.
Previous release: v0.1.0
DATA_MANIFEST.json identifies the exact bytes uploaded. JSON records that
contained compute-host paths have only those path strings replaced with release
relative paths or UNBUNDLED/<filename> labels. The original file hash is
retained beside each transformed record's release hash. Numeric arrays and model
weights are copied byte-for-byte and checked against the frozen run manifest.
The processed data correspond to the code package's
configs/paper_run_v1.json path layout. To place only the data files in
a checkout of that package without replacing its README:
hf download Boom5426/VCDesign --repo-type dataset \
--include 'inputs/**' 'checkpoints/**' 'records/**' 'DATA_MANIFEST.json' \
--local-dir /path/to/code-package
python3 tools/verify_processed_data.py --root /path/to/code-package
The exact repository revision should be pinned for a reproduction run. The
selected checkpoint was epoch 8, with frozen SHA-256
58da13d252faf9bcaa74c53345c0afc380882bdd5f410a77cfa5044f9a6e9484.
Original public sources
- Replogle et al. K562 genome-scale and RPE1 Perturb-seq
- GEO GSE264667 HepG2 and Jurkat screens
- STRING v12.0 physical interaction data
- MAP and MAP-KG
- ESM-2
The derived files are tied to those source versions; the K562 genome-scale screen is distinct from the K562 essential screen. Cite the original data and knowledge resources when using this collection.
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