lit_sb3.utils.logger

source module lit_sb3.utils.logger

Classes

Video — Video data class storing the video frames and the frame per seconds.
Figure — Figure data class storing a matplotlib figure and whether to close the figure after logging it.
Image — Image data class storing an image and data format.
HParam — Hyperparameter data class storing hyperparameters and metrics in dictionaries.
FormatUnsupportedError — Custom error to display informative message when.
KVWriter — Key Value writer.
SeqWriter — sequence writer.
HumanOutputFormat — A human-readable output format producing ASCII tables of key-value pairs.
JSONOutputFormat — Log to a file, in the JSON format.
CSVOutputFormat — Log to a file, in a CSV format.
TensorBoardOutputFormat — Dumps key/value pairs into TensorBoard's numeric format.
Logger — The logger class.

Functions

filter_excluded_keys — Filters the keys specified by key_exclude for the specified format.
make_output_format — Return a logger for the requested format.
configure — Configure the current logger.
read_json — Read a json file using pandas.
read_csv — Read a csv file using pandas.

source class Video(frames: torch.Tensor, fps: float)

Video data class storing the video frames and the frame per seconds.

Parameters

frames : torch.Tensor — frames to create the video from
fps : float — frames per second

source class Figure(figure: matplotlib.figure.Figure, close: bool)

Figure data class storing a matplotlib figure and whether to close the figure after logging it.

Parameters

figure : matplotlib.figure.Figure — figure to log
close : bool — if true, close the figure after logging it

source class Image(image: torch.Tensor | np.ndarray | str, dataformats: str)

Image data class storing an image and data format.

Parameters

image : torch.Tensor | np.ndarray | str — image to log
dataformats : str — Image data format specification of the form NCHW, NHWC, CHW, HWC, HW, WH, etc. More info in add_image method doc at https://pytorch.org/docs/stable/tensorboard.html Gym envs normally use 'HWC' (channel last)

source class HParam(hparam_dict: Mapping[str, bool | str | float | None], metric_dict: Mapping[str, float])

Hyperparameter data class storing hyperparameters and metrics in dictionaries.

Parameters

hparam_dict : Mapping[str, bool | str | float | None] — key-value pairs of hyperparameters to log
metric_dict : Mapping[str, float] — key-value pairs of metrics to log A non-empty metrics dict is required to display hyperparameters in the corresponding Tensorboard section.

source class FormatUnsupportedError(unsupported_formats: Sequence[str], value_description: str)

Bases : NotImplementedError

Custom error to display informative message when.

a value is not supported by some formats.

Parameters

unsupported_formats : Sequence[str] — A sequence of unsupported formats, for instance ["stdout"].
value_description : str — Description of the value that cannot be logged by this format.

source class KVWriter()

Key Value writer.

Methods

write — Write a dictionary to file.
close — Close owned resources.

source method KVWriter.write(key_values: dict[str, Any], key_excluded: dict[str, tuple[str, ...] | None], step: int = 0) → None

Write a dictionary to file.

Parameters

key_values : dict[str, Any] — Dictionary of key-value pairs to write.
key_excluded : dict[str, tuple[str, ...] | None] — Dictionary mapping keys to output format exclusions.
step : int — Current step number.

Raises

NotImplementedError

source method KVWriter.close() → None

Close owned resources.

Raises

NotImplementedError

source class SeqWriter()

sequence writer.

Methods

write_sequence — write_sequence an array to file.

source method SeqWriter.write_sequence(sequence: list[str]) → None

write_sequence an array to file.

Parameters

sequence : list[str]
sequence : list[str] — Sequence of values to write.

Raises

NotImplementedError

source class HumanOutputFormat(filename_or_file: str | TextIO, max_length: int = 36)

Bases : KVWriter, SeqWriter

A human-readable output format producing ASCII tables of key-value pairs.

Set attribute max_length to change the maximum length of keys and values to write to output (or specify it when calling __init__).

Parameters

filename_or_file : str | TextIO — the file to write the log to
max_length : int — the maximum length of keys and values to write to output. Outputs longer than this will be truncated. An error will be raised if multiple keys are truncated to the same value. The maximum output width will be 2*max_length + 7. The default of 36 produces output no longer than 79 characters wide.

Methods

write
write_sequence
close — Closes the file.

source method HumanOutputFormat.write(key_values: dict[str, Any], key_excluded: dict[str, tuple[str, ...] | None], step: int = 0) → None

Raises

ValueError
FormatUnsupportedError

source method HumanOutputFormat.write_sequence(sequence: list[str]) → None

source method HumanOutputFormat.close() → None

Closes the file.

source filter_excluded_keys(key_values: dict[str, Any], key_excluded: dict[str, tuple[str, ...] | None], _format: str) → dict[str, Any]

Filters the keys specified by key_exclude for the specified format.

Parameters

key_values : dict[str, Any] — log dictionary to be filtered
key_excluded : dict[str, tuple[str, ...] | None] — keys to be excluded per format
_format : str — format for which this filter is run

Returns

dict[str, Any] — dict without the excluded keys

source class JSONOutputFormat(filename: str)

Bases : KVWriter

Log to a file, in the JSON format.

Parameters

filename : str — the file to write the log to

Methods

write
close — Closes the file.

source method JSONOutputFormat.write(key_values: dict[str, Any], key_excluded: dict[str, tuple[str, ...] | None], step: int = 0) → None

Raises

FormatUnsupportedError

source method JSONOutputFormat.close() → None

Closes the file.

source class CSVOutputFormat(filename: str)

Bases : KVWriter

Log to a file, in a CSV format.

Parameters

filename : str — the file to write the log to

Methods

write
close — Closes the file.

source method CSVOutputFormat.write(key_values: dict[str, Any], key_excluded: dict[str, tuple[str, ...] | None], step: int = 0) → None

Raises

FormatUnsupportedError

source method CSVOutputFormat.close() → None

Closes the file.

source class TensorBoardOutputFormat(folder: str)

Bases : KVWriter

Dumps key/value pairs into TensorBoard's numeric format.

Parameters

folder : str — the folder to write the log to

Methods

write
close — Closes the file.

source method TensorBoardOutputFormat.write(key_values: dict[str, Any], key_excluded: dict[str, tuple[str, ...] | None], step: int = 0) → None

source method TensorBoardOutputFormat.close() → None

Closes the file.

source make_output_format(_format: str, log_dir: str, log_suffix: str = '') → KVWriter

Return a logger for the requested format.

Parameters

_format : str — the requested format to log to ('stdout', 'log', 'json' or 'csv' or 'tensorboard')
log_dir : str — the logging directory
log_suffix : str — the suffix for the log file

Returns

KVWriter — the logger

Raises

ValueError

source class Logger(folder: str | None, output_formats: list[KVWriter])

The logger class.

Parameters

folder : str | None — the logging location
output_formats : list[KVWriter] — the list of output formats

Methods

to_tuple — Helper function to convert str to tuple of str.
record — Log a value of some diagnostic.
record_mean — The same as record(), but if called many times, values averaged.
dump — Write all of the diagnostics from the current iteration.
log — Write the sequence of args, with no separators,.
debug — Write the sequence of args, with no separators,.
info — Write the sequence of args, with no separators,.
warn — Write the sequence of args, with no separators,.
error — Write the sequence of args, with no separators,.
set_level — Set logging threshold on current logger.
get_dir — Get directory that log files are being written to.
close — Closes the file.

source staticmethod Logger.to_tuple(string_or_tuple: str | tuple[str, ...] | None) → tuple[str, ...]

Helper function to convert str to tuple of str.

source method Logger.record(key: str, value: Any, exclude: str | tuple[str, ...] | None = None) → None

Log a value of some diagnostic.

Call this once for each diagnostic quantity, each iteration If called many times, last value will be used.

Parameters

key : str — save to log this key
value : Any — save to log this value
exclude : str | tuple[str, ...] | None — outputs to be excluded

source method Logger.record_mean(key: str, value: float | None, exclude: str | tuple[str, ...] | None = None) → None

The same as record(), but if called many times, values averaged.

Parameters

key : str — save to log this key
value : float | None — save to log this value
exclude : str | tuple[str, ...] | None — outputs to be excluded

source method Logger.dump(step: int = 0) → None

Write all of the diagnostics from the current iteration.

source method Logger.log(*args, level: int = INFO) → None