Datasets:
Dataset Viewer
The dataset viewer is not available for this dataset.
Cannot get the config names for the dataset.
Error code: ConfigNamesError
Exception: ValueError
Message: Some splits are duplicated in data_files: ['train', 'train', 'train', 'train', 'train', 'train', 'train', 'train', 'train', 'train', 'train', 'train', 'train', 'train', 'train', 'train', 'train', 'train', 'train', 'train', 'train', 'train', 'train', 'train', 'train', 'train', 'train', 'train', 'train', 'train', 'train', 'train', 'train', 'train', 'train', 'train', 'train', 'train', 'train', 'train', 'train', 'train', 'train', 'train', 'train', 'train', 'train', 'train', 'train', 'train', 'train', 'train', 'train', 'train', 'train', 'train', 'train', 'train', 'train', 'train', 'train', 'train', 'train', 'train', 'train', 'train', 'train', 'train', 'train', 'train', 'train', 'train', 'train', 'train', 'train', 'train', 'train', 'train', 'train']
Traceback: Traceback (most recent call last):
File "/src/services/worker/src/worker/job_runners/dataset/config_names.py", line 67, in compute_config_names_response
config_names = get_dataset_config_names(
path=dataset,
token=hf_token,
)
File "/usr/local/lib/python3.14/site-packages/datasets/inspect.py", line 161, in get_dataset_config_names
dataset_module = dataset_module_factory(
path,
...<4 lines>...
**download_kwargs,
)
File "/usr/local/lib/python3.14/site-packages/datasets/load.py", line 1215, in dataset_module_factory
raise e1 from None
File "/usr/local/lib/python3.14/site-packages/datasets/load.py", line 1190, in dataset_module_factory
).get_module()
~~~~~~~~~~^^
File "/usr/local/lib/python3.14/site-packages/datasets/load.py", line 646, in get_module
patterns = sanitize_patterns(next(iter(metadata_configs.values()))["data_files"])
File "/usr/local/lib/python3.14/site-packages/datasets/data_files.py", line 151, in sanitize_patterns
raise ValueError(f"Some splits are duplicated in data_files: {splits}")
ValueError: Some splits are duplicated in data_files: ['train', 'train', 'train', 'train', 'train', 'train', 'train', 'train', 'train', 'train', 'train', 'train', 'train', 'train', 'train', 'train', 'train', 'train', 'train', 'train', 'train', 'train', 'train', 'train', 'train', 'train', 'train', 'train', 'train', 'train', 'train', 'train', 'train', 'train', 'train', 'train', 'train', 'train', 'train', 'train', 'train', 'train', 'train', 'train', 'train', 'train', 'train', 'train', 'train', 'train', 'train', 'train', 'train', 'train', 'train', 'train', 'train', 'train', 'train', 'train', 'train', 'train', 'train', 'train', 'train', 'train', 'train', 'train', 'train', 'train', 'train', 'train', 'train', 'train', 'train', 'train', 'train', 'train', 'train']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.
time_series_agent_evals (TsFile)
Apache TsFile version of RockfishData/TimeSeriesAgentEvals.
- Converted rows: 103,255
- Data files:
['cell_site_data_1.tsfile', 'cell_site_data_2.tsfile', 'cell_site_data_3.tsfile', 'cell_site_data_4.tsfile', 'cell_site_data_5.tsfile', 'cell_site_data_6.tsfile', 'cell_site_data_7.tsfile', 'cell_site_data_8.tsfile', 'cell_site_data_9.tsfile', 'cell_site_with_inc_data_1.tsfile', 'cell_site_with_inc_data_2.tsfile', 'cell_site_with_inc_data_3.tsfile', 'cell_site_with_inc_data_4.tsfile', 'cell_site_with_inc_data_5.tsfile', 'cell_site_with_inc_data_6.tsfile', 'cell_site_with_inc_data_7.tsfile', 'cell_site_with_inc_data_8.tsfile', 'cell_site_with_inc_data_9.tsfile', 'core_node_data_1.tsfile', 'core_node_data_2.tsfile', 'core_node_data_3.tsfile', 'core_node_data_4.tsfile', 'core_node_data_5.tsfile', 'core_node_data_6.tsfile', 'core_node_data_7.tsfile', 'core_node_data_8.tsfile', 'core_node_with_inc_data_1.tsfile', 'core_node_with_inc_data_2.tsfile', 'core_node_with_inc_data_3.tsfile', 'core_node_with_inc_data_4.tsfile', 'core_node_with_inc_data_5.tsfile', 'core_node_with_inc_data_6.tsfile', 'core_node_with_inc_data_7.tsfile', 'core_node_with_inc_data_8.tsfile', 'ecommerce_sessions_data_1.tsfile', 'ecommerce_sessions_data_2.tsfile', 'ecommerce_sessions_data_3.tsfile', 'ecommerce_sessions_data_4.tsfile', 'ecommerce_sessions_data_5.tsfile', 'ecommerce_sessions_data_6.tsfile', 'ecommerce_sessions_data_7.tsfile', 'ecommerce_sessions_data_8.tsfile', 'ecommerce_sessions_data_9.tsfile', 'ecommerce_users_data_1.tsfile', 'ecommerce_users_data_2.tsfile', 'ecommerce_users_data_3.tsfile', 'ecommerce_users_data_4.tsfile', 'ecommerce_users_data_5.tsfile', 'ecommerce_users_data_6.tsfile', 'ecommerce_users_data_7.tsfile', 'ecommerce_users_data_8.tsfile', 'ecommerce_users_data_9.tsfile', 'iot_device_data_1.tsfile', 'iot_device_data_2.tsfile', 'iot_device_data_3.tsfile', 'iot_device_data_4.tsfile', 'iot_device_data_5.tsfile', 'iot_device_data_6.tsfile', 'iot_device_data_7.tsfile', 'iot_device_data_8.tsfile', 'iot_device_data_9.tsfile', 'transport_link_data_1.tsfile', 'transport_link_data_2.tsfile', 'transport_link_data_3.tsfile', 'transport_link_data_4.tsfile', 'transport_link_data_5.tsfile', 'transport_link_data_6.tsfile', 'transport_link_data_7.tsfile', 'transport_link_data_8.tsfile', 'transport_link_data_9.tsfile', 'transport_link_with_inc_data_1.tsfile', 'transport_link_with_inc_data_2.tsfile', 'transport_link_with_inc_data_3.tsfile', 'transport_link_with_inc_data_4.tsfile', 'transport_link_with_inc_data_5.tsfile', 'transport_link_with_inc_data_6.tsfile', 'transport_link_with_inc_data_7.tsfile', 'transport_link_with_inc_data_8.tsfile', 'transport_link_with_inc_data_9.tsfile']
Converted tables are the nine datasets/*.csv series files. The queries/*.csv evaluation query templates are not converted.
Usage
Install the Apache TsFile Python SDK (pip install tsfile) and read a converted file:
from pathlib import Path
from tsfile import TsFileReader
path = Path("cell_site_data_1.tsfile")
with TsFileReader(str(path)) as reader:
schemas = reader.get_all_table_schemas()
print("tables:", list(schemas))
table_name = next(iter(schemas))
table = schemas[table_name]
columns = [column.get_column_name() for column in table.get_columns()]
print("columns:", columns)
field_names = [
column.get_column_name()
for column in table.get_columns()
if column.get_column_name() not in {"Time", "time"}
]
if field_names:
with reader.query_table(table_name, field_names[:3], batch_size=1024) as result:
batch = result.read_arrow_batch()
if batch is not None:
print(batch.to_pandas().head())
Source & license
- Original dataset: https://huggingface.co/datasets/RockfishData/TimeSeriesAgentEvals
- Author / publisher: RockfishData
- License: apache-2.0
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