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csv_logger

CSVLogger

Bases: Trace

Log monitored quantities in a CSV file.

Parameters:

Name Type Description Default
filename str

Output filename.

 required
monitor_names Optional[Union[List[str], str]]

List of keys to monitor. If None then all metrics will be recorded.

 None
mode Union[str, Set[str]]

What mode(s) to execute this Trace in. For example, "train", "eval", "test", or "infer". To execute regardless of mode, pass None. To execute in all modes except for a particular one, you can pass an argument like "!infer" or "!train".

 ('eval', 'test')
Source code in fastestimator\fastestimator\trace\io\csv_logger.py 
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class CSVLogger(Trace):
    """Log monitored quantities in a CSV file.

    Args:
        filename: Output filename.
        monitor_names: List of keys to monitor. If None then all metrics will be recorded.
        mode: What mode(s) to execute this Trace in. For example, "train", "eval", "test", or "infer". To execute
            regardless of mode, pass None. To execute in all modes except for a particular one, you can pass an argument
            like "!infer" or "!train".
    """
    def __init__(self,
                 filename: str,
                 monitor_names: Optional[Union[List[str], str]] = None,
                 mode: Union[str, Set[str]] = ("eval", "test")) -> None:
        super().__init__(inputs="*" if monitor_names is None else monitor_names, mode=mode)
        self.filename = filename
        self.data = None

    def on_begin(self, data: Data) -> None:
        self.data = defaultdict(list)

    def on_epoch_end(self, data: Data) -> None:
        self.data["mode"].append(self.system.mode)
        self.data["epoch"].append(self.system.epoch_idx)
        if "*" in self.inputs:
            for key, value in data.read_logs().items():
                self.data[key].append(value)
        else:
            for key in self.inputs:
                self.data[key].append(data[key])

    def on_end(self, data: Data) -> None:
        df = pd.DataFrame(data=self.data)
        if os.path.exists(self.filename):
            df.to_csv(self.filename, mode='a', index=False)
        else:
            df.to_csv(self.filename, index=False)