planner.core.logger#

Module Contents#

class planner.core.logger.Logger(graph: jacta.planner.core.graph.Graph, params: jacta.planner.core.parameter_container.ParameterContainer, search_index: int = 0, log_path: str = '/workspaces/bdai/projects/jacta/log/', log_file: str | None = None)#
Parameters:

  • graph (jacta.planner.core.graph.Graph) –

  • params (jacta.planner.core.parameter_container.ParameterContainer) –

  • search_index (int) –

  • log_path (str) –

  • log_file (Optional[str]) –

reset() → None#
Return type:

None

get_log_name() → str | None#

Get where the log is stored

Return type:

Optional[str]

log_params() → None#
Return type:

None

Parameters:

iteration_number (int) –

Return type:

None

log_node_selection(node_ids: torch.IntTensor, strategy: jacta.planner.core.types.SelectionType) → None#
Parameters:

  • node_ids (torch.IntTensor) –

  • strategy (jacta.planner.core.types.SelectionType) –

Return type:

None

log_action_sampler(node_ids: torch.IntTensor, strategy: jacta.planner.core.types.ActionType) → None#
Parameters:

  • node_ids (torch.IntTensor) –

  • strategy (jacta.planner.core.types.ActionType) –

Return type:

None

log_node_extension(node_ids: torch.IntTensor, best_id: torch.IntTensor, dynamics_time: float) → None#
Parameters:

  • node_ids (torch.IntTensor) –

  • best_id (torch.IntTensor) –

  • dynamics_time (float) –

Return type:

None

log_node_pruning(node_id: int, strategy: str, num_removed_nodes: int) → None#
Parameters:

  • node_id (int) –

  • strategy (str) –

  • num_removed_nodes (int) –

Return type:

None

create_distance_log() → None#
Return type:

None

create_reward_log() → None#
Return type:

None

simple_progress_statistics() → None#
Return type:

None

simple_path_statistics() → Tuple[torch.FloatTensor, torch.FloatTensor]#
Return type:

Tuple[torch.FloatTensor, torch.FloatTensor]