releso.spor.SPORObjectExternalPythonFunction

class releso.spor.SPORObjectExternalPythonFunction(*, save_location: Path, logger_name: str | None = None, name: str, stop_after_error: bool = True, reward_on_error: float, reward_on_completion: float | None = None, run_on_reset: bool = True, additional_observations: ObservationDefinition | ObservationDefinitionMulti | List[ObservationDefinition | ObservationDefinitionMulti] | None = None, multi_processor: MultiProcessor | MPIClusterMultiProcessor | None = None, use_communication_interface: bool = False, add_step_information: bool = False, working_directory: str, python_file_path: str | Path)

Bases: SPORObjectPythonFunction

Load a python function from an external file.

This class is meant to load a python function from an external file and execute it. The function will be imported in to the package and run in the same python environment as the rest of the package is. If you want to call the function you can use the SPORCommandLineObject.

The function needs to be called main and needs to have the following signature:

args, logger, func_data

Where args are the SPOR COMM arguments, logger is a logger provided by the SPORObject and, func_data is a persistent data variable which is not touched by the SPORObject.

__init__(**data: Any) → None: Constructor for the ReLeSO basemodel object.

Methods

`convert_to_pathlib_add_datetime`(v)	Add timestamp to save_location, of applicable.
`get_default_observation`(observations)	Generate default observations if no were returned or step failed.
`get_logger`()	Gets the currently defined environment logger.
`get_multiprocessing_prefix`(core_count)	Add commandline prefix for mpi multiprocessor.
`get_observations`()	Return number of observation the step generates.
`run`(step_information, environment_id[, ...])	This function loads and executes the defined python file.
`set_logger_name_recursively`(logger_name)	Set the logger_name variable for all child elements.
`setup_working_directory`(environment_id)	Set up the working directory for the SPORStep.
`spor_com_interface`(reset, environment_id, ...)	Add spor com interface command line options.
`spor_com_interface_add`(returned_step_dict, ...)	Add returned step information of the spor com interface.
`spor_com_interface_read`(output, step_dict)	Read in return values of the spor com interface.

Attributes

`python_file_path`
`multi_processor`	Definition of the multi-processor.
`use_communication_interface`	whether or not to use the SPOR communication interface.
`add_step_information`	interface commandline options including the observations, info, done, reward
`working_directory`	Path to the directory in which the program should run, helpful for programs using relative paths.
`name`	name of the SPOR step.
`stop_after_error`	Whether to stop after this step if this step has thrown an error.
`reward_on_error`	Reward which is returned if any kind of error is thrown during completing the defined task.
`reward_on_completion`	Reward which is returned if the task completed with out an error.
`run_on_reset`	This boolean can disable the running of the task when a reset is performed.
`additional_observations`	How many additional observations does this object yield.
`save_location`	Definition of the save location of the logs and validation results.
`logger_name`	name of the logger.

run(step_information: Tuple[ndarray | Dict[str, ndarray] | Tuple[ndarray, ...], float, bool, Dict], environment_id: UUID4, validation_id: int | None = None, core_count: int = 1, reset: bool = False) → Tuple[ndarray | Dict[str, ndarray] | Tuple[ndarray, ...], float, bool, Dict]

This function loads and executes the defined python file.

The herein defined python file needs to have a function called main with three parameters.

args: Namespace see SPORCommInterface
logger: logger to be used in the function
func_data: data variable can be used to store persistent data

Parameters:

step_information (StepReturnType) – Previously collected step values. Should be up-to-date.
environment_id (UUID4) – Environment ID which is used to distinguish different environments which run in parallel.
validation_id (Optional[int], optional) – During validation the validation id signifies the validation episode. If none the current episode is not a validation episode. Defaults to None.
core_count (int, optional) – Can only be one since no multi processing is possible in this case.
reset (bool, optional) – Boolean on whether or not this object is called because of a reset. Defaults to False.

Returns:

The full compliment of the step information are returned. The include observation, reward, done, info.

Return type:

StepReturnType