smoother.models.reduction._utils
================================

.. py:module:: smoother.models.reduction._utils


Functions
---------

.. autoapisummary::

   smoother.models.reduction._utils.iterate
   smoother.models.reduction._utils.broadcast_labels
   smoother.models.reduction._utils.get_max_epochs_heuristic
   smoother.models.reduction._utils.set_params_online_update


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

.. py:function:: iterate(obj, func)

   Iterates over an object and applies a function to each element.


.. py:function:: broadcast_labels(o, n_broadcast=-1)

   Utility for the semi-supervised setting.

   If y is defined(labelled batch) then one-hot encode the labels (no broadcasting needed)
   If y is undefined (unlabelled batch) then generate all possible labels (and broadcast other
   arguments if not None)


.. py:function:: get_max_epochs_heuristic(n_obs: int, epochs_cap: int = 400, decay_at_n_obs: int = 20000) -> int

   Compute a heuristic for the default number of maximum epochs.

   If `n_obs <= decay_at_n_obs`, the number of maximum epochs is set to
   `epochs_cap`. Otherwise, the number of maximum epochs decays according to
   `(decay_at_n_obs / n_obs) * epochs_cap`, with a minimum of 1.

   :param n_obs: The number of observations in the dataset.
   :param epochs_cap: The maximum number of epochs for the heuristic.
   :param decay_at_n_obs: The number of observations at which the heuristic starts decaying.

   :returns: A heuristic for the default number of maximum epochs.
   :rtype: `int`


.. py:function:: set_params_online_update(module, unfrozen, freeze_decoder_first_layer, freeze_batchnorm_encoder, freeze_batchnorm_decoder, freeze_dropout, freeze_expression, freeze_classifier)

   Freeze parts of network for scArches.