Dataset Viewer
The dataset viewer is not available for this split.
Cannot load the dataset split (in streaming mode) to extract the first rows.
Error code: StreamingRowsError
Exception: CastError
Message: Couldn't cast
case_id: string
strict_mode: bool
experiment_metadata: struct<accession: string, reference: string, summary: string>
child 0, accession: string
child 1, reference: string
child 2, summary: string
counts_file: string
sample_ids: list<item: string>
child 0, item: string
sample_metadata: struct<SAM24360838: string, SAM24360839: string, SAM24360844: string, SAM24360845: string>
child 0, SAM24360838: string
child 1, SAM24360839: string
child 2, SAM24360844: string
child 3, SAM24360845: string
conditions: list<item: string>
child 0, item: string
default_contrast: struct<reference: string, alternate: string>
child 0, reference: string
child 1, alternate: string
analysis_options: struct<min_total_count: int64, padj_alpha: double, de_query_direction: string>
child 0, min_total_count: int64
child 1, padj_alpha: double
child 2, de_query_direction: string
enrichr_libraries: list<item: string>
child 0, item: string
true_pathway: string
eval_mode: bool
max_steps: int64
to
{'case_id': Value('string'), 'strict_mode': Value('bool'), 'experiment_metadata': {'accession': Value('string'), 'reference': Value('string'), 'summary': Value('string')}, 'counts_file': Value('string'), 'sample_ids': List(Value('string')), 'sample_metadata': {'GSM3024053': Value('string'), 'GSM3024054': Value('string'), 'GSM3024055': Value('string'), 'GSM3024056': Value('string'), 'GSM3024057': Value('string'), 'GSM3024058': Value('string'), 'GSM3024059': Value('string'), 'GSM3024060': Value('string')}, 'conditions': List(Value('string')), 'default_contrast': {'reference': Value('string'), 'alternate': Value('string')}, 'analysis_options': {'min_total_count': Value('int64'), 'padj_alpha': Value('float64'), 'de_query_direction': Value('string')}, 'enrichr_libraries': List(Value('string')), 'true_pathway': Value('string')}
because column names don't match
Traceback: Traceback (most recent call last):
File "/src/services/worker/src/worker/utils.py", line 149, in get_rows_or_raise
return get_rows(
dataset=dataset,
...<4 lines>...
column_names=column_names,
)
File "/src/libs/libcommon/src/libcommon/utils.py", line 272, in decorator
return func(*args, **kwargs)
File "/src/services/worker/src/worker/utils.py", line 129, in get_rows
rows_plus_one = list(itertools.islice(safe_iter(ds, dataset=dataset), rows_max_number + 1))
File "/src/services/worker/src/worker/utils.py", line 489, in safe_iter
yield from ds.decode(False) if ds.features else ds
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2818, in __iter__
for key, example in ex_iterable:
^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2355, in __iter__
for key, pa_table in self._iter_arrow():
~~~~~~~~~~~~~~~~^^
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2380, in _iter_arrow
for key, pa_table in self.ex_iterable._iter_arrow():
~~~~~~~~~~~~~~~~~~~~~~~~~~~~^^
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 536, in _iter_arrow
for key, pa_table in iterator:
^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 419, in _iter_arrow
for key, pa_table in self.generate_tables_fn(**gen_kwags):
~~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^
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 2369, in table_cast
return cast_table_to_schema(table, schema)
File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2297, in cast_table_to_schema
raise CastError(
...<3 lines>...
)
datasets.table.CastError: Couldn't cast
case_id: string
strict_mode: bool
experiment_metadata: struct<accession: string, reference: string, summary: string>
child 0, accession: string
child 1, reference: string
child 2, summary: string
counts_file: string
sample_ids: list<item: string>
child 0, item: string
sample_metadata: struct<SAM24360838: string, SAM24360839: string, SAM24360844: string, SAM24360845: string>
child 0, SAM24360838: string
child 1, SAM24360839: string
child 2, SAM24360844: string
child 3, SAM24360845: string
conditions: list<item: string>
child 0, item: string
default_contrast: struct<reference: string, alternate: string>
child 0, reference: string
child 1, alternate: string
analysis_options: struct<min_total_count: int64, padj_alpha: double, de_query_direction: string>
child 0, min_total_count: int64
child 1, padj_alpha: double
child 2, de_query_direction: string
enrichr_libraries: list<item: string>
child 0, item: string
true_pathway: string
eval_mode: bool
max_steps: int64
to
{'case_id': Value('string'), 'strict_mode': Value('bool'), 'experiment_metadata': {'accession': Value('string'), 'reference': Value('string'), 'summary': Value('string')}, 'counts_file': Value('string'), 'sample_ids': List(Value('string')), 'sample_metadata': {'GSM3024053': Value('string'), 'GSM3024054': Value('string'), 'GSM3024055': Value('string'), 'GSM3024056': Value('string'), 'GSM3024057': Value('string'), 'GSM3024058': Value('string'), 'GSM3024059': Value('string'), 'GSM3024060': Value('string')}, 'conditions': List(Value('string')), 'default_contrast': {'reference': Value('string'), 'alternate': Value('string')}, 'analysis_options': {'min_total_count': Value('int64'), 'padj_alpha': Value('float64'), 'de_query_direction': Value('string')}, 'enrichr_libraries': List(Value('string')), 'true_pathway': Value('string')}
because column names don't matchNeed help to make the dataset viewer work? Make sure to review how to configure the dataset viewer, and open a discussion for direct support.
OpenEnv Pathway Analysis Environment
This repository packages pathway_analysis_env for Hugging Face Hub publication.
Contents
envs/pathway_analysis_env/environment code- reproducible GEO benchmark inputs for 3 tasks
- task expansion scripts:
create_geo_task.pyappend_task_to_manifest.py
Run locally
uv sync --all-extras
PYTHONPATH=src:envs uv run python envs/pathway_analysis_env/scripts/run_agent_eval_suite.py --manifest envs/pathway_analysis_env/data/eval_manifest_geo3.json
Note on Spaces
Publishing as a Docker Space from a free user namespace may require Hugging Face PRO. This repo is ready for maintainers to deploy under an entitled namespace.
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