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Source code for ignite.handlers.early_stopping

from collections import OrderedDict
from typing import Callable, cast, Mapping, Optional

from ignite.base import Serializable
from ignite.engine import Engine
from ignite.utils import setup_logger

__all__ = ["EarlyStopping"]


[docs]class EarlyStopping(Serializable): """EarlyStopping handler can be used to stop the training if no improvement after a given number of events. Args: patience: Number of events to wait if no improvement and then stop the training. score_function: It should be a function taking a single argument, an :class:`~ignite.engine.engine.Engine` object, and return a score `float`. An improvement is considered if the score is higher. trainer: Trainer engine to stop the run if no improvement. min_delta: A minimum increase in the score to qualify as an improvement, i.e. an increase of less than or equal to `min_delta`, will count as no improvement. cumulative_delta: It True, `min_delta` defines an increase since the last `patience` reset, otherwise, it defines an increase after the last event. Default value is False. Examples: .. code-block:: python from ignite.engine import Engine, Events from ignite.handlers import EarlyStopping def score_function(engine): val_loss = engine.state.metrics['nll'] return -val_loss handler = EarlyStopping(patience=10, score_function=score_function, trainer=trainer) # Note: the handler is attached to an *Evaluator* (runs one epoch on validation dataset). evaluator.add_event_handler(Events.COMPLETED, handler) """ _state_dict_all_req_keys = ( "counter", "best_score", ) def __init__( self, patience: int, score_function: Callable, trainer: Engine, min_delta: float = 0.0, cumulative_delta: bool = False, ): if not callable(score_function): raise TypeError("Argument score_function should be a function.") if patience < 1: raise ValueError("Argument patience should be positive integer.") if min_delta < 0.0: raise ValueError("Argument min_delta should not be a negative number.") if not isinstance(trainer, Engine): raise TypeError("Argument trainer should be an instance of Engine.") self.score_function = score_function self.patience = patience self.min_delta = min_delta self.cumulative_delta = cumulative_delta self.trainer = trainer self.counter = 0 self.best_score = None # type: Optional[float] self.logger = setup_logger(__name__ + "." + self.__class__.__name__) def __call__(self, engine: Engine) -> None: score = self.score_function(engine) if self.best_score is None: self.best_score = score elif score <= self.best_score + self.min_delta: if not self.cumulative_delta and score > self.best_score: self.best_score = score self.counter += 1 self.logger.debug("EarlyStopping: %i / %i" % (self.counter, self.patience)) if self.counter >= self.patience: self.logger.info("EarlyStopping: Stop training") self.trainer.terminate() else: self.best_score = score self.counter = 0
[docs] def state_dict(self) -> "OrderedDict[str, float]": """Method returns state dict with ``counter`` and ``best_score``. Can be used to save internal state of the class. """ return OrderedDict([("counter", self.counter), ("best_score", cast(float, self.best_score))])
[docs] def load_state_dict(self, state_dict: Mapping) -> None: """Method replace internal state of the class with provided state dict data. Args: state_dict: a dict with "counter" and "best_score" keys/values. """ super().load_state_dict(state_dict) self.counter = state_dict["counter"] self.best_score = state_dict["best_score"]

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