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class MetricTracker:
 | def __init__(
 |     self,
 |     patience: Optional[int] = None,
 |     metric_name: str = None,
 |     should_decrease: bool = None
 | ) -> None

This class tracks a metric during training for the dual purposes of early stopping and for knowing whether the current value is the best so far. It mimics the PyTorch state_dict / load_state_dict interface, so that it can be checkpointed along with your model and optimizer.

Some metrics improve by increasing; others by decreasing. Here you can either explicitly supply should_decrease, or you can provide a metric_name in which case "should decrease" is inferred from the first character, which must be "+" or "-".


  • patience : int, optional (default = None)
    If provided, then should_stop_early() returns True if we go this many epochs without seeing a new best value.
  • metric_name : str, optional (default = None)
    If provided, it's used to infer whether we expect the metric values to increase (if it starts with "+") or decrease (if it starts with "-"). It's an error if it doesn't start with one of those. If it's not provided, you should specify should_decrease instead.
  • should_decrease : str, optional (default = None)
    If metric_name isn't provided (in which case we can't infer should_decrease), then you have to specify it here.


class MetricTracker:
 | ...
 | def clear(self) -> None

Clears out the tracked metrics, but keeps the patience and should_decrease settings.


class MetricTracker:
 | ...
 | def state_dict(self) -> Dict[str, Any]

A Trainer can use this to serialize the state of the metric tracker.


class MetricTracker:
 | ...
 | def load_state_dict(self, state_dict: Dict[str, Any]) -> None

A Trainer can use this to hydrate a metric tracker from a serialized state.


class MetricTracker:
 | ...
 | def add_metric(self, metric: float) -> None

Record a new value of the metric and update the various things that depend on it.


class MetricTracker:
 | ...
 | def add_metrics(self, metrics: Iterable[float]) -> None

Helper to add multiple metrics at once.


class MetricTracker:
 | ...
 | def is_best_so_far(self) -> bool

Returns true if the most recent value of the metric is the best so far.


class MetricTracker:
 | ...
 | def should_stop_early(self) -> bool

Returns true if improvement has stopped for long enough.