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
$schema: string
$id: string
title: string
type: string
additionalProperties: bool
required: list<item: string>
child 0, item: string
allOf: list<item: struct<description: string, if: struct<properties: struct<is_runnable: struct<const: bool (... 74 chars omitted)
child 0, item: struct<description: string, if: struct<properties: struct<is_runnable: struct<const: bool>>>, then: (... 62 chars omitted)
child 0, description: string
child 1, if: struct<properties: struct<is_runnable: struct<const: bool>>>
child 0, properties: struct<is_runnable: struct<const: bool>>
child 0, is_runnable: struct<const: bool>
child 0, const: bool
child 2, then: struct<properties: struct<blockers: struct<minItems: int64>>>
child 0, properties: struct<blockers: struct<minItems: int64>>
child 0, blockers: struct<minItems: int64>
child 0, minItems: int64
properties: struct<edge_id: struct<type: string>, judged_at: struct<type: string>, metadata_version: struct<type (... 1522 chars omitted)
child 0, edge_id: struct<type: string>
child 0, type: string
child 1, judged_at: struct<type: string>
child 0, type: string
child 2, metadata_version: struct<type: string, description: string>
child 0, type: string
child 1, description: string
child 3, is_runnable: struct<type: string>
child 0, type: string
child 4, blockers: struct<type: string, items: struct<type: string>>
...
: list<item: string>, enum: list<item: string>, description: string>
child 0, type: list<item: string>
child 0, item: string
child 1, enum: list<item: string>
child 0, item: string
child 2, description: string
child 1, dataset: struct<type: list<item: string>>
child 0, type: list<item: string>
child 0, item: string
child 2, field: struct<type: list<item: string>>
child 0, type: list<item: string>
child 0, item: string
child 3, exists_in_v7: struct<type: string, description: string>
child 0, type: string
child 1, description: string
child 4, is_container: struct<type: string>
child 0, type: string
child 5, value_filter_required: struct<type: string>
child 0, type: string
child 6, value_filter_present: struct<type: list<item: string>>
child 0, type: list<item: string>
child 0, item: string
child 7, inferred_value_filter: struct<type: list<item: string>>
child 0, type: list<item: string>
child 0, item: string
child 8, v7_match_count_expected: struct<type: list<item: string>, description: string>
child 0, type: list<item: string>
child 0, item: string
child 1, description: string
description: string
to
{'$schema': Value('string'), '$id': Value('string'), 'title': Value('string'), 'description': Value('string'), 'type': Value('string'), 'additionalProperties': Value('bool'), 'required': List(Value('string')), 'properties': {'env': {'type': Value('string'), 'enum': List(Value('string')), 'description': Value('string')}, 'mode': {'type': Value('string'), 'enum': List(Value('string'))}, 'generated_at': {'type': Value('string')}, 'metadata_version': {'type': Value('string')}, 'data_root': {'type': Value('string'), 'description': Value('string')}, 'probe_version': {'type': Value('string')}, 'privacy': {'type': Value('string'), 'additionalProperties': Value('bool'), 'required': List(Value('string')), 'description': Value('string'), 'properties': {'min_cell_count': {'type': Value('string'), 'minimum': Value('int64'), 'description': Value('string')}, 'small_cell_policy': {'type': Value('string'), 'enum': List(Value('string')), 'description': Value('string')}, 'suppression_applied': {'type': List(Value('string')), 'description': Value('string')}, 'export_whitelist': {'type': List(Value('string')), 'items': {'type': Value('string')}, 'description': Value('string')}}}, 'datasets': {'type': Value('string'), 'items': {'$ref': Value('string')}}, 'overlap_matrix': {'type': Value('string'), 'additionalProperties': Value('bool'), 'required': List(Value('string')), 'description': Value('string'), 'properties': {'entries': {'type': Value('string'), 'items': {'type': Value('string'), 'additiona
...
e': List(Value('string'))}, 'exists': {'type': Value('string')}, 'excluded': {'type': Value('string'), 'description': Value('string')}, 'excluded_reason': {'type': List(Value('string'))}, 'n_rows': {'type': List(Value('string')), 'minimum': Value('int64')}, 'participant_id_coverage': {'type': List(Value('string')), 'additionalProperties': Value('bool'), 'properties': {'n_unique': {'type': Value('string'), 'minimum': Value('int64')}, 'covers_full_cohort': {'type': List(Value('string'))}}}, 'cohort_coverage': {'type': List(Value('string')), 'items': {'type': Value('string')}}, 'timepoints': {'type': List(Value('string')), 'items': {'type': Value('string')}, 'description': Value('string')}, 'batch_platform_assay': {'type': List(Value('string')), 'additionalProperties': Value('bool'), 'description': Value('string')}, 'fields': {'type': Value('string'), 'items': {'type': Value('string'), 'additionalProperties': Value('bool'), 'required': List(Value('string')), 'properties': {'name': {'type': Value('string')}, 'dtype': {'type': Value('string')}, 'unit': {'type': List(Value('string'))}, 'coding_dict': {'type': List(Value('string')), 'additionalProperties': Value('bool'), 'description': Value('string')}, 'n_total': {'type': Value('string'), 'minimum': Value('int64')}, 'n_non_null': {'type': Value('string'), 'minimum': Value('int64')}, 'missing_rate': {'type': List(Value('string')), 'minimum': Value('int64'), 'maximum': Value('int64')}, 'is_container': {'type': Value('string')}}}}}}}}
because column names don't match
Traceback: Traceback (most recent call last):
File "/src/services/worker/src/worker/utils.py", line 147, 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 127, 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 478, 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
$schema: string
$id: string
title: string
type: string
additionalProperties: bool
required: list<item: string>
child 0, item: string
allOf: list<item: struct<description: string, if: struct<properties: struct<is_runnable: struct<const: bool (... 74 chars omitted)
child 0, item: struct<description: string, if: struct<properties: struct<is_runnable: struct<const: bool>>>, then: (... 62 chars omitted)
child 0, description: string
child 1, if: struct<properties: struct<is_runnable: struct<const: bool>>>
child 0, properties: struct<is_runnable: struct<const: bool>>
child 0, is_runnable: struct<const: bool>
child 0, const: bool
child 2, then: struct<properties: struct<blockers: struct<minItems: int64>>>
child 0, properties: struct<blockers: struct<minItems: int64>>
child 0, blockers: struct<minItems: int64>
child 0, minItems: int64
properties: struct<edge_id: struct<type: string>, judged_at: struct<type: string>, metadata_version: struct<type (... 1522 chars omitted)
child 0, edge_id: struct<type: string>
child 0, type: string
child 1, judged_at: struct<type: string>
child 0, type: string
child 2, metadata_version: struct<type: string, description: string>
child 0, type: string
child 1, description: string
child 3, is_runnable: struct<type: string>
child 0, type: string
child 4, blockers: struct<type: string, items: struct<type: string>>
...
: list<item: string>, enum: list<item: string>, description: string>
child 0, type: list<item: string>
child 0, item: string
child 1, enum: list<item: string>
child 0, item: string
child 2, description: string
child 1, dataset: struct<type: list<item: string>>
child 0, type: list<item: string>
child 0, item: string
child 2, field: struct<type: list<item: string>>
child 0, type: list<item: string>
child 0, item: string
child 3, exists_in_v7: struct<type: string, description: string>
child 0, type: string
child 1, description: string
child 4, is_container: struct<type: string>
child 0, type: string
child 5, value_filter_required: struct<type: string>
child 0, type: string
child 6, value_filter_present: struct<type: list<item: string>>
child 0, type: list<item: string>
child 0, item: string
child 7, inferred_value_filter: struct<type: list<item: string>>
child 0, type: list<item: string>
child 0, item: string
child 8, v7_match_count_expected: struct<type: list<item: string>, description: string>
child 0, type: list<item: string>
child 0, item: string
child 1, description: string
description: string
to
{'$schema': Value('string'), '$id': Value('string'), 'title': Value('string'), 'description': Value('string'), 'type': Value('string'), 'additionalProperties': Value('bool'), 'required': List(Value('string')), 'properties': {'env': {'type': Value('string'), 'enum': List(Value('string')), 'description': Value('string')}, 'mode': {'type': Value('string'), 'enum': List(Value('string'))}, 'generated_at': {'type': Value('string')}, 'metadata_version': {'type': Value('string')}, 'data_root': {'type': Value('string'), 'description': Value('string')}, 'probe_version': {'type': Value('string')}, 'privacy': {'type': Value('string'), 'additionalProperties': Value('bool'), 'required': List(Value('string')), 'description': Value('string'), 'properties': {'min_cell_count': {'type': Value('string'), 'minimum': Value('int64'), 'description': Value('string')}, 'small_cell_policy': {'type': Value('string'), 'enum': List(Value('string')), 'description': Value('string')}, 'suppression_applied': {'type': List(Value('string')), 'description': Value('string')}, 'export_whitelist': {'type': List(Value('string')), 'items': {'type': Value('string')}, 'description': Value('string')}}}, 'datasets': {'type': Value('string'), 'items': {'$ref': Value('string')}}, 'overlap_matrix': {'type': Value('string'), 'additionalProperties': Value('bool'), 'required': List(Value('string')), 'description': Value('string'), 'properties': {'entries': {'type': Value('string'), 'items': {'type': Value('string'), 'additiona
...
e': List(Value('string'))}, 'exists': {'type': Value('string')}, 'excluded': {'type': Value('string'), 'description': Value('string')}, 'excluded_reason': {'type': List(Value('string'))}, 'n_rows': {'type': List(Value('string')), 'minimum': Value('int64')}, 'participant_id_coverage': {'type': List(Value('string')), 'additionalProperties': Value('bool'), 'properties': {'n_unique': {'type': Value('string'), 'minimum': Value('int64')}, 'covers_full_cohort': {'type': List(Value('string'))}}}, 'cohort_coverage': {'type': List(Value('string')), 'items': {'type': Value('string')}}, 'timepoints': {'type': List(Value('string')), 'items': {'type': Value('string')}, 'description': Value('string')}, 'batch_platform_assay': {'type': List(Value('string')), 'additionalProperties': Value('bool'), 'description': Value('string')}, 'fields': {'type': Value('string'), 'items': {'type': Value('string'), 'additionalProperties': Value('bool'), 'required': List(Value('string')), 'properties': {'name': {'type': Value('string')}, 'dtype': {'type': Value('string')}, 'unit': {'type': List(Value('string'))}, 'coding_dict': {'type': List(Value('string')), 'additionalProperties': Value('bool'), 'description': Value('string')}, 'n_total': {'type': Value('string'), 'minimum': Value('int64')}, 'n_non_null': {'type': Value('string'), 'minimum': Value('int64')}, 'missing_rate': {'type': List(Value('string')), 'minimum': Value('int64'), 'maximum': Value('int64')}, 'is_container': {'type': 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.
YAML Metadata Warning:empty or missing yaml metadata in repo card
Check out the documentation for more information.
eval_agent — 抽取出来的核心集
DoAtlas validation / evaluation agent 的自包含核心，从 Doatlas2 剥离了边抽取数据、CEV 重建引擎、产品文档等无关内容。
eval_agent pipeline（pseudo dataset）
🧭 维护从
docs/SPEC.md开始（控制面 / 事实来源，docs-first：先改文档再改代码）。本 README 只管"怎么用"。
目录
validation_agent/— agent 本体（cli + core 各 agent + schemas + configs + templates + validators + tests/fixtures）。零外部 import，测试用包内 fixtures。docs/SPEC.md— 规范控制面索引（架构总览 + 规范→代码→测试映射 + 改动流程）。维护入口。docs/DECISIONS.md— 决策记录（append-only ADR）。docs/validation_agent/— 详细规范（00–14 + README），SPEC.md指向它们取深度。
安装
pip install -r requirements.txt
跑测试（离线，用录制好的 fixtures，不需要数据集）
python -m pytest validation_agent/tests/ -q
真跑一条边（需要 ANTHROPIC_API_KEY + 合成数据集，本目录未包含）
# 1. 把合成数据集放到 ./data/pseudo_current（指向 pseudo_dataset_v9）
# 2. 准备一份 step2_edges.json（边输入）
# 3. 调整 validation_agent/configs/current_batch.json 里的 data_root / batch_root
python -m validation_agent.cli run --pmid <pmid> --edge_id "<edge_id>"
测试现状（pytest validation_agent/tests/ → 约 667 passed，8 failed + 11 errors）
没有逻辑回归；失败全部可归为下面三类，按需处理：
- A. 录制失效（11 errors + 5 failed） — planner / semantic_reviewer 等 demo-replay 测试，因 prompt 与估计量 embed 被改动、录制哈希（
derive_call_id）旋转而对不上。 修复：在有数据集的环境（如原仓库）跑python -m validation_agent.cli regenerate-fixtures重对齐，或做一次真跑刷新录制。注意regenerate-fixtures只换 key 不刷新模型回复（见下）。 - B. 需要预先批跑产物（2 failed） —
test_build_evidence_package_*读orchestrator/batch_runs/demo_perreault/runs/...，该目录是生成产物，原仓库里也不存在，需先跑一次批量验证才有。 - C. 需要 git 仓库（1 failed） —
test_review_acceptance_happy要git rev-parse HEAD取真实提交。本目录非 git 仓库；若git init && git commit后即可通过。
✅ 已修（原 D 项配置漂移） —
test_current_batch_config_complete曾断言v7_root=='pseudo_dataset_v8'，但 v9 重建已把运行期数据根迁到data/pseudo_current（config 正确）。测试已对齐到 config；注意这与注入占位符cli.V7_ROOT_PLACEHOLDER（仍为pseudo_dataset_v8）是两回事。
离线、与上述无关的单元/golden 测试（估计量、codegen、executor、schema、亚组交互等）全部通过。
说明
- 已删除
tests/test_orchestrator.py（它依赖orchestrator/构建 harness，不属于运行期 agent）。 current_batch.json里的data_root/batch_root/hpp_field_index指向原仓库路径，真跑前按需改（运行时不读 hpp_field_index）。regenerate-fixtures与真跑最好在有合成数据集的环境做（执行步骤需要 parquet 数据）；纯本目录（未带数据集）只适合跑离线单元/golden 测试。
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