log_parse

parse_log_dir

A function which will gather all log files within a given folder and pass them along for visualization.

Parameters:

Name	Type	Description	Default
dir_path	str	The path to a directory containing log files.	required
log_extension	str	The extension of the log files.	'.txt'
recursive_search	bool	Whether to recursively search sub-directories for log files.	False
smooth_factor	float	A non-negative float representing the magnitude of gaussian smoothing to apply (zero for none).	1
save	bool	Whether to save (True) or display (False) the generated graph.	False
save_path	Optional[str]	Where to save the image if save is true. Defaults to dir_path if not provided.	None
ignore_metrics	Optional[Set[str]]	Any metrics within the log files which will not be visualized.	None
share_legend	bool	Whether to have one legend across all graphs (True) or one legend per graph (False).	True
pretty_names	bool	Whether to modify the metric names in graph titles (True) or leave them alone (False).	False

Source code in fastestimator\fastestimator\summary\logs\log_parse.py

def parse_log_dir(dir_path: str,
                  log_extension: str = '.txt',
                  recursive_search: bool = False,
                  smooth_factor: float = 1,
                  save: bool = False,
                  save_path: Optional[str] = None,
                  ignore_metrics: Optional[Set[str]] = None,
                  share_legend: bool = True,
                  pretty_names: bool = False) -> None:
    """A function which will gather all log files within a given folder and pass them along for visualization.

    Args:
        dir_path: The path to a directory containing log files.
        log_extension: The extension of the log files.
        recursive_search: Whether to recursively search sub-directories for log files.
        smooth_factor: A non-negative float representing the magnitude of gaussian smoothing to apply (zero for none).
        save: Whether to save (True) or display (False) the generated graph.
        save_path: Where to save the image if save is true. Defaults to dir_path if not provided.
        ignore_metrics: Any metrics within the log files which will not be visualized.
        share_legend: Whether to have one legend across all graphs (True) or one legend per graph (False).
        pretty_names: Whether to modify the metric names in graph titles (True) or leave them alone (False).
    """
    loader = DirDataset(root_dir=dir_path, file_extension=log_extension, recursive_search=recursive_search)
    file_paths = list(map(lambda d: d['x'], loader.data.values()))

    parse_log_files(file_paths,
                    log_extension,
                    smooth_factor,
                    save,
                    save_path,
                    ignore_metrics,
                    share_legend,
                    pretty_names)

parse_log_file

A function which will parse log files into a dictionary of metrics.

Parameters:

Name	Type	Description	Default
file_path	str	The path to a log file.	required
file_extension	str	The extension of the log file.	required

Returns:

Type	Description
Summary	An experiment summarizing the given log file.

Source code in fastestimator\fastestimator\summary\logs\log_parse.py

def parse_log_file(file_path: str, file_extension: str) -> Summary:
    """A function which will parse log files into a dictionary of metrics.

    Args:
        file_path: The path to a log file.
        file_extension: The extension of the log file.
    Returns:
        An experiment summarizing the given log file.
    """
    # TODO: need to handle multi-line output like confusion matrix
    experiment = Summary(strip_suffix(os.path.split(file_path)[1].strip(), file_extension))
    with open(file_path) as file:
        for line in file:
            mode = None
            if line.startswith("FastEstimator-Train") or line.startswith("FastEstimator-Finish"):
                mode = "train"
            elif line.startswith("FastEstimator-Eval"):
                mode = "eval"
            elif line.startswith("FastEstimator-Test"):
                mode = "test"
            if mode is None:
                continue
            parsed_line = re.findall(r"([^:^;\s]+):[\s]*([-]?[0-9]+[.]?[0-9]*);", line)
            step = parsed_line[0]
            assert step[0] == "step", \
                "Log file (%s) seems to be missing step information, or step is not listed first" % file
            for metric in parsed_line[1:]:
                experiment.history[mode][metric[0]].update({int(step[1]): float(metric[1])})
    return experiment

parse_log_files

Parse one or more log files for graphing.

This function which will iterate through the given log file paths, parse them to extract metrics, remove any metrics which are blacklisted, and then pass the necessary information on the graphing function.

Parameters:

Name	Type	Description	Default
file_paths	List[str]	A list of paths to various log files.	required
log_extension	Optional[str]	The extension of the log files.	'.txt'
smooth_factor	float	A non-negative float representing the magnitude of gaussian smoothing to apply (zero for none).	0
save	bool	Whether to save (True) or display (False) the generated graph.	False
save_path	Optional[str]	Where to save the image if save is true. Defaults to dir_path if not provided.	None
ignore_metrics	Optional[Set[str]]	Any metrics within the log files which will not be visualized.	None
share_legend	bool	Whether to have one legend across all graphs (True) or one legend per graph (False).	True
pretty_names	bool	Whether to modify the metric names in graph titles (True) or leave them alone (False).	False

Raises:

Type	Description
AssertionError	If no log files are provided.

Source code in fastestimator\fastestimator\summary\logs\log_parse.py

def parse_log_files(file_paths: List[str],
                    log_extension: Optional[str] = '.txt',
                    smooth_factor: float = 0,
                    save: bool = False,
                    save_path: Optional[str] = None,
                    ignore_metrics: Optional[Set[str]] = None,
                    share_legend: bool = True,
                    pretty_names: bool = False) -> None:
    """Parse one or more log files for graphing.

    This function which will iterate through the given log file paths, parse them to extract metrics, remove any
    metrics which are blacklisted, and then pass the necessary information on the graphing function.

    Args:
        file_paths: A list of paths to various log files.
        log_extension: The extension of the log files.
        smooth_factor: A non-negative float representing the magnitude of gaussian smoothing to apply (zero for none).
        save: Whether to save (True) or display (False) the generated graph.
        save_path: Where to save the image if save is true. Defaults to dir_path if not provided.
        ignore_metrics: Any metrics within the log files which will not be visualized.
        share_legend: Whether to have one legend across all graphs (True) or one legend per graph (False).
        pretty_names: Whether to modify the metric names in graph titles (True) or leave them alone (False).

    Raises:
        AssertionError: If no log files are provided.
    """
    if file_paths is None or len(file_paths) < 1:
        raise AssertionError("must provide at least one log file")
    if save and save_path is None:
        save_path = file_paths[0]

    experiments = []
    for file_path in file_paths:
        experiments.append(parse_log_file(file_path, log_extension))
    visualize_logs(experiments,
                   save_path=save_path,
                   smooth_factor=smooth_factor,
                   share_legend=share_legend,
                   pretty_names=pretty_names,
                   ignore_metrics=ignore_metrics)