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Cannot extract the features (columns) for the split 'train' of the config 'default' of the dataset.
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')

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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 as name for SayPlan).
  • detailed: Detailed task description (same as name for 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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