The dataset viewer is not available for this split.
Error code: FeaturesError
Exception: ArrowTypeError
Message: ("Expected bytes, got a 'int' object", 'Conversion failed for column init with type object')
Traceback: Traceback (most recent call last):
File "/usr/local/lib/python3.12/site-packages/datasets/packaged_modules/json/json.py", line 151, in _generate_tables
pa_table = paj.read_json(
^^^^^^^^^^^^^^
File "pyarrow/_json.pyx", line 342, in pyarrow._json.read_json
File "pyarrow/error.pxi", line 155, in pyarrow.lib.pyarrow_internal_check_status
File "pyarrow/error.pxi", line 92, in pyarrow.lib.check_status
pyarrow.lib.ArrowInvalid: JSON parse error: Column() changed from object to array in row 0
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "/src/services/worker/src/worker/job_runners/split/first_rows.py", line 243, in compute_first_rows_from_streaming_response
iterable_dataset = iterable_dataset._resolve_features()
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 3496, in _resolve_features
features = _infer_features_from_batch(self.with_format(None)._head())
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 2257, in _head
return next(iter(self.iter(batch_size=n)))
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 2461, in iter
for key, example in iterator:
^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 1952, in __iter__
for key, pa_table in self._iter_arrow():
^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 1974, in _iter_arrow
yield from self.ex_iterable._iter_arrow()
File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 503, in _iter_arrow
for key, pa_table in iterator:
^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 350, in _iter_arrow
for key, pa_table in self.generate_tables_fn(**gen_kwags):
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/packaged_modules/json/json.py", line 181, in _generate_tables
pa_table = pa.Table.from_pandas(df, preserve_index=False)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "pyarrow/table.pxi", line 4795, in pyarrow.lib.Table.from_pandas
File "/usr/local/lib/python3.12/site-packages/pyarrow/pandas_compat.py", line 637, in dataframe_to_arrays
arrays = [convert_column(c, f)
^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/pyarrow/pandas_compat.py", line 625, in convert_column
raise e
File "/usr/local/lib/python3.12/site-packages/pyarrow/pandas_compat.py", line 619, in convert_column
result = pa.array(col, type=type_, from_pandas=True, safe=safe)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "pyarrow/array.pxi", line 365, in pyarrow.lib.array
File "pyarrow/array.pxi", line 91, in pyarrow.lib._ndarray_to_array
File "pyarrow/error.pxi", line 92, in pyarrow.lib.check_status
pyarrow.lib.ArrowTypeError: ("Expected bytes, got a 'int' object", 'Conversion failed for column init with type object')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.
GraSIF Repository
This folder contains three graph datasets saved as pickle files, used for the evaluation of methods. Each dataset is a list of dictionaries containing the task name, initial state, and goal state represented as NetworkX graphs. Additionally, each dictionary includes specific information relevant to the dataset.
| Data | Number of Tasks | Mean nodes | Actions |
|---|---|---|---|
| SayPlan Office | 25 | 202.6 | 2.1 |
| Behaviour-1K | 186 | 12.1 | 4.9 |
| VirtualHome | 347 | 195.7 | 1.6 |
Table 1: Dataset comparison. Actions represent the mean number of nodes changed between the initial and goal graph.
To load a dataset, use the following code snippet:
import pickle
with open('./datasets/<name>.pkl', 'rb') as file:
tasks = pickle.load(file)
SayPlan Office
The SayPlan Office dataset represents graphs and tasks defined in SayPlan. Each task consists of a dictionary with the following structure:
name: The name of the task.human: Human-readable task description (same asnamefor SayPlan).detailed: Detailed task description (same asnamefor SayPlan).init: Initial state as a NetworkX graph.goal: Goal state as a NetworkX graph.actions: A list of ground-truth actions to complete the task.
Behaviour-1K
The Behaviour-1K dataset represents tasks defined in Behaviour1K. For each task defined in BDDL, a subgraph was constructed to represent the environment. Using this subgraph, the goal graph was created, and human-readable as well as detailed task descriptions were added.
The dataset contains a total of 186 tasks, each represented by a dictionary with the following structure:
name: The name of the task from Behaviour1K.human: Human-readable task description.detailed: Detailed task description.init: Initial state as a NetworkX graph.goal: Goal state as a NetworkX graph.
VirtualHome RobotHow
The VirtualHome dataset represents tasks from the RobotHow dataset. For each task, the VirtualHome graph was reconstructed into a structure compatible with our methods. This was achieved using the graph_parser.py script available in the utils folder of the repository.
Additionally, an ids dictionary maps nodes from the initial NetworkX graph to VirtualHome IDs. For example, the node ('fridge', 1) in the initial graph corresponds to the fridge node with ID 67 in the VirtualHome backend graph. This mapping is useful when using the dataset with the VirtualHome simulator.
Each task is represented by a dictionary with the following structure:
name: The name of the task from RobotHow.human: Human-readable task description. (Same as name for RobotHow)detailed: Detailed task description.init: Initial state as a NetworkX graph.goal: Goal state as a NetworkX graph.ids: Mapping of nodes to VirtualHome IDs.
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