artist.optim.training
=====================

.. py:module:: artist.optim.training


Classes
-------

.. autoapisummary::

   artist.optim.training.EarlyStopping


Functions
---------

.. autoapisummary::

   artist.optim.training.exponential
   artist.optim.training.cyclic
   artist.optim.training.reduce_on_plateau


Module Contents
---------------

.. py:function:: exponential(optimizer: torch.optim.Optimizer, parameters: dict[str, float]) -> torch.optim.lr_scheduler.LRScheduler

   Create an exponential learning rate scheduler.

   Parameters
   ----------
   optimizer : Optimizer
       The optimizer.
   parameters : dict[str, float]
       The scheduler parameters.

   Returns
   -------
   LRScheduler
       An exponential learning rate scheduler.


.. py:function:: cyclic(optimizer: torch.optim.Optimizer, parameters: dict[str, float]) -> torch.optim.lr_scheduler.LRScheduler

   Create a cyclic learning rate scheduler.

   Parameters
   ----------
   optimizer : Optimizer
       The optimizer.
   parameters : dict[str, float]
       The scheduler parameters.

   Returns
   -------
   LRScheduler
       A cyclic learning rate scheduler.


.. py:function:: reduce_on_plateau(optimizer: torch.optim.Optimizer, parameters: dict[str, float]) -> torch.optim.lr_scheduler.LRScheduler

   Create learning rate scheduler that reduces on plateaus.

   Parameters
   ----------
   optimizer : Optimizer
       The optimizer.
   parameters : dict[str, float]
       The scheduler parameters.

   Returns
   -------
   LRScheduler
       A learning rate scheduler that reduces on plateaus.


.. py:class:: EarlyStopping(window_size: int = 10, patience: int = 20, min_improvement: float = 0.0001, relative: bool = True, eps: float = 1e-08)

   Initialize the early stopping.

   Parameters
   ----------
   window_size : int
       Number of epochs used to estimate loss trend (default is 10).
   patience : int
       Number of consecutive non-improving windows before stopping (default is 20).
   min_improvement : float
       Minimum required improvement over the window to reset patience (default is 1e-4).
   relative : bool
       Indicates whether improvement is normalized by loss magnitude (default is True).
   eps : float
       Small value for stability (default is 1e-8).


   .. py:attribute:: window_size
      :value: 10



   .. py:attribute:: patience
      :value: 20



   .. py:attribute:: min_improvement
      :value: 0.0001



   .. py:attribute:: relative
      :value: True



   .. py:attribute:: eps
      :value: 1e-08



   .. py:attribute:: loss_history
      :type:  Deque[float]


   .. py:attribute:: counter
      :value: 0



   .. py:method:: step(loss: float) -> bool

      Update stopping state.

      Parameters
      ----------
      loss : float
          Current loss value.

      Returns
      -------
      bool
          True if optimization should stop, otherwise False.