Source code for netket.callbacks.earlystopping

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from typing import Optional, Union

import numpy as np

from netket.utils import struct


[docs] class EarlyStopping(struct.Pytree, mutable=True): """A simple callback to stop NetKet if there are no more improvements in the training. based on `driver._loss_name`. """ min_delta: float """Minimum change in the monitored quantity to qualify as an improvement.""" min_reldelta: float """Minimum relative change in the monitored quantity to qualify as an improvement. This behaves similarly to `min_delta` but is more useful for intensive quantities that converge to 0, where absolute tolerances might not be effective. """ patience: Union[int, float] """Number of epochs with no improvement after which training will be stopped.""" baseline: Optional[float] """Baseline value for the monitored quantity. Training will stop if the driver hits the baseline.""" monitor: str """Loss statistic to monitor. Should be one of 'mean', 'variance', 'sigma'.""" # The quantities below are internal and should not be edited directly # by the user _best_val: float = np.inf """Best value of the loss observed up to this iteration. """ _best_iter: int """Iteration at which the `_best_val` was observed.""" _best_patience_counter: int """Stores the iteration at which we've seen the best loss so far"""
[docs] def __init__( self, min_delta: float = 0.0, min_reldelta: float = 0.0, patience: Union[int, float] = 0, baseline: Optional[float] = None, monitor: str = "mean", ): """ Construct an early stopping callback. Args: min_delta: Minimum change in the monitored quantity to qualify as an improvement. min_reldelta: Minimum relative change in the monitored quantity to qualify as an improvement. patience: Number of epochs with no improvement after which training will be stopped. baseline: Baseline value for the monitored quantity. Training will stop if the driver hits the baseline. monitor: Loss statistic to monitor. Should be one of `mean`, `variance`, `sigma`. """ self.min_delta = min_delta self.min_reldelta = min_reldelta self.patience = patience self.baseline = baseline self.monitor = monitor self._best_val = np.infty self._best_iter = 0 self._best_patience_counter = 0
[docs] def __call__(self, step, log_data, driver): """ A boolean function that determines whether or not to stop training. Args: step: An integer corresponding to the step (iteration or epoch) in training. log_data: A dictionary containing log data for training. driver: A NetKet variational driver. Returns: A boolean. If True, training continues, else, it does not. """ loss = np.real(getattr(log_data[driver._loss_name], self.monitor)) self._best_patience_counter += 1 if self._is_improvement(loss, self._best_val): self._best_val = loss self._best_iter = step if self.baseline is None: self._best_patience_counter = 0 elif self._is_improvement(loss, self.baseline): # If using baseline, update patience only if we are better than baseline self._best_patience_counter = 0 if self._best_patience_counter > self.patience: return False return True
def _is_improvement(self, loss, target): # minimal value for absolute and relative improvement abs_minval = target - self.min_delta rel_minval = target * (1 - self.min_reldelta) # minimval value that qualify as an improvement minval = min(abs_minval, rel_minval) return loss < minval