Datasets:

---

sparsh122

/

maas-openenv-rl-dataset

Like 0

Error code:   DatasetGenerationError
Exception:    CastError
Message:      Couldn't cast
episode_id: string
source_row_id: int64
split: string
difficulty: string
example_type: string
messages: list<item: struct<role: string, content: string>>
  child 0, item: struct<role: string, content: string>
      child 0, role: string
      child 1, content: string
target: string
metadata: struct<source_labels: struct<condition: string, urgency: string>, teacher_condition: string, teacher (... 143 chars omitted)
  child 0, source_labels: struct<condition: string, urgency: string>
      child 0, condition: string
      child 1, urgency: string
  child 1, teacher_condition: string
  child 2, teacher_urgency: string
  child 3, hidden_signals: list<item: string>
      child 0, item: string
  child 4, source_teacher_condition_match: bool
  child 5, source_teacher_urgency_match: bool
  child 6, reward: double
trajectory: null
label_alignment: struct<condition_match: bool, urgency_match: bool>
  child 0, condition_match: bool
  child 1, urgency_match: bool
initial_hidden_signals: list<item: string>
  child 0, item: string
reward_components: struct<class_weight: double, condition_score: double, urgency_score: double, under_escalation_penalt (... 93 chars omitted)
  child 0, class_weight: double
  child 1, condition_score: double
  child 2, urgency_score: double
  child 3, under_escalation_penalty: double
  child 4, danger_override_penalty: double
  child 5, data_recency_bonus: double
  child 6, total_reward: double
source_labels: struct<condition: string, urgency: string>
  child 0, condition: string
  child 1, urgency: string
reference_labels: struct<condition: string, urgency: string>
  child 0, condition: string
  child 1, urgency: string
action_space: struct<action_type: list<item: string>, condition: list<item: string>, urgency: list<item: string>>
  child 0, action_type: list<item: string>
      child 0, item: string
  child 1, condition: list<item: string>
      child 0, item: string
  child 2, urgency: list<item: string>
      child 0, item: string
final_reward: double
initial_action_type: string
observation_full: struct<user_id: int64, weeks_pregnant: int64, trimester: int64, region: string, risk_flags: list<ite (... 225 chars omitted)
  child 0, user_id: int64
  child 1, weeks_pregnant: int64
  child 2, trimester: int64
  child 3, region: string
  child 4, risk_flags: list<item: string>
      child 0, item: string
  child 5, bp_trend: string
  child 6, avg_kick_count: double
  child 7, avg_meals: double
  child 8, avg_sleep: double
  child 9, latest_weight_kg: double
  child 10, latest_energy: int64
  child 11, latest_breathlessness: int64
  child 12, history_flags: list<item: string>
      child 0, item: string
  child 13, days_of_data: int64
to
{'episode_id': Value('string'), 'source_row_id': Value('int64'), 'split': Value('string'), 'difficulty': Value('string'), 'action_space': {'action_type': List(Value('string')), 'condition': List(Value('string')), 'urgency': List(Value('string'))}, 'source_labels': {'condition': Value('string'), 'urgency': Value('string')}, 'reference_labels': {'condition': Value('string'), 'urgency': Value('string')}, 'label_alignment': {'condition_match': Value('bool'), 'urgency_match': Value('bool')}, 'observation_full': {'user_id': Value('int64'), 'weeks_pregnant': Value('int64'), 'trimester': Value('int64'), 'region': Value('string'), 'risk_flags': List(Value('string')), 'bp_trend': Value('string'), 'avg_kick_count': Value('float64'), 'avg_meals': Value('float64'), 'avg_sleep': Value('float64'), 'latest_weight_kg': Value('float64'), 'latest_energy': Value('int64'), 'latest_breathlessness': Value('int64'), 'history_flags': List(Value('string')), 'days_of_data': Value('int64')}, 'initial_hidden_signals': List(Value('string')), 'initial_action_type': Value('string'), 'trajectory': List(Json(decode=True)), 'final_reward': Value('float64'), 'reward_components': {'class_weight': Value('float64'), 'condition_score': Value('float64'), 'urgency_score': Value('float64'), 'under_escalation_penalty': Value('float64'), 'danger_override_penalty': Value('float64'), 'data_recency_bonus': Value('float64'), 'total_reward': Value('float64')}}
because column names don't match
Traceback:    Traceback (most recent call last):
                File "/usr/local/lib/python3.12/site-packages/datasets/builder.py", line 1779, in _prepare_split_single
                  for key, table in generator:
                                    ^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/packaged_modules/json/json.py", line 295, in _generate_tables
                  self._cast_table(pa_table, json_field_paths=json_field_paths),
                  ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/packaged_modules/json/json.py", line 128, in _cast_table
                  pa_table = table_cast(pa_table, self.info.features.arrow_schema)
                             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/table.py", line 2281, in table_cast
                  return cast_table_to_schema(table, schema)
                         ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/table.py", line 2227, in cast_table_to_schema
                  raise CastError(
              datasets.table.CastError: Couldn't cast
              episode_id: string
              source_row_id: int64
              split: string
              difficulty: string
              example_type: string
              messages: list<item: struct<role: string, content: string>>
                child 0, item: struct<role: string, content: string>
                    child 0, role: string
                    child 1, content: string
              target: string
              metadata: struct<source_labels: struct<condition: string, urgency: string>, teacher_condition: string, teacher (... 143 chars omitted)
                child 0, source_labels: struct<condition: string, urgency: string>
                    child 0, condition: string
                    child 1, urgency: string
                child 1, teacher_condition: string
                child 2, teacher_urgency: string
                child 3, hidden_signals: list<item: string>
                    child 0, item: string
                child 4, source_teacher_condition_match: bool
                child 5, source_teacher_urgency_match: bool
                child 6, reward: double
              trajectory: null
              label_alignment: struct<condition_match: bool, urgency_match: bool>
                child 0, condition_match: bool
                child 1, urgency_match: bool
              initial_hidden_signals: list<item: string>
                child 0, item: string
              reward_components: struct<class_weight: double, condition_score: double, urgency_score: double, under_escalation_penalt (... 93 chars omitted)
                child 0, class_weight: double
                child 1, condition_score: double
                child 2, urgency_score: double
                child 3, under_escalation_penalty: double
                child 4, danger_override_penalty: double
                child 5, data_recency_bonus: double
                child 6, total_reward: double
              source_labels: struct<condition: string, urgency: string>
                child 0, condition: string
                child 1, urgency: string
              reference_labels: struct<condition: string, urgency: string>
                child 0, condition: string
                child 1, urgency: string
              action_space: struct<action_type: list<item: string>, condition: list<item: string>, urgency: list<item: string>>
                child 0, action_type: list<item: string>
                    child 0, item: string
                child 1, condition: list<item: string>
                    child 0, item: string
                child 2, urgency: list<item: string>
                    child 0, item: string
              final_reward: double
              initial_action_type: string
              observation_full: struct<user_id: int64, weeks_pregnant: int64, trimester: int64, region: string, risk_flags: list<ite (... 225 chars omitted)
                child 0, user_id: int64
                child 1, weeks_pregnant: int64
                child 2, trimester: int64
                child 3, region: string
                child 4, risk_flags: list<item: string>
                    child 0, item: string
                child 5, bp_trend: string
                child 6, avg_kick_count: double
                child 7, avg_meals: double
                child 8, avg_sleep: double
                child 9, latest_weight_kg: double
                child 10, latest_energy: int64
                child 11, latest_breathlessness: int64
                child 12, history_flags: list<item: string>
                    child 0, item: string
                child 13, days_of_data: int64
              to
              {'episode_id': Value('string'), 'source_row_id': Value('int64'), 'split': Value('string'), 'difficulty': Value('string'), 'action_space': {'action_type': List(Value('string')), 'condition': List(Value('string')), 'urgency': List(Value('string'))}, 'source_labels': {'condition': Value('string'), 'urgency': Value('string')}, 'reference_labels': {'condition': Value('string'), 'urgency': Value('string')}, 'label_alignment': {'condition_match': Value('bool'), 'urgency_match': Value('bool')}, 'observation_full': {'user_id': Value('int64'), 'weeks_pregnant': Value('int64'), 'trimester': Value('int64'), 'region': Value('string'), 'risk_flags': List(Value('string')), 'bp_trend': Value('string'), 'avg_kick_count': Value('float64'), 'avg_meals': Value('float64'), 'avg_sleep': Value('float64'), 'latest_weight_kg': Value('float64'), 'latest_energy': Value('int64'), 'latest_breathlessness': Value('int64'), 'history_flags': List(Value('string')), 'days_of_data': Value('int64')}, 'initial_hidden_signals': List(Value('string')), 'initial_action_type': Value('string'), 'trajectory': List(Json(decode=True)), 'final_reward': Value('float64'), 'reward_components': {'class_weight': Value('float64'), 'condition_score': Value('float64'), 'urgency_score': Value('float64'), 'under_escalation_penalty': Value('float64'), 'danger_override_penalty': Value('float64'), 'data_recency_bonus': Value('float64'), 'total_reward': Value('float64')}}
              because column names don't match
              
              The above exception was the direct cause of the following exception:
              
              Traceback (most recent call last):
                File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 1347, in compute_config_parquet_and_info_response
                  parquet_operations = convert_to_parquet(builder)
                                       ^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 980, in convert_to_parquet
                  builder.download_and_prepare(
                File "/usr/local/lib/python3.12/site-packages/datasets/builder.py", line 882, in download_and_prepare
                  self._download_and_prepare(
                File "/usr/local/lib/python3.12/site-packages/datasets/builder.py", line 943, in _download_and_prepare
                  self._prepare_split(split_generator, **prepare_split_kwargs)
                File "/usr/local/lib/python3.12/site-packages/datasets/builder.py", line 1646, in _prepare_split
                  for job_id, done, content in self._prepare_split_single(
                                               ^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/builder.py", line 1832, in _prepare_split_single
                  raise DatasetGenerationError("An error occurred while generating the dataset") from e
              datasets.exceptions.DatasetGenerationError: An error occurred while generating the dataset