:py:mod:`mlair.plotting.training_monitoring`
============================================

.. py:module:: mlair.plotting.training_monitoring

.. autoapi-nested-parse::

   Plots to monitor training.



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

Classes
~~~~~~~

.. autoapisummary::

   mlair.plotting.training_monitoring.PlotModelHistory
   mlair.plotting.training_monitoring.PlotModelLearningRate




Attributes
~~~~~~~~~~

.. autoapisummary::

   mlair.plotting.training_monitoring.__author__
   mlair.plotting.training_monitoring.__date__
   mlair.plotting.training_monitoring.history_object
   mlair.plotting.training_monitoring.lr_object


.. py:data:: __author__
   :annotation: = Felix Kleinert, Lukas Leufen

   

.. py:data:: __date__
   :annotation: = 2019-12-11

   

.. py:data:: history_object
   

   

.. py:data:: lr_object
   

   

.. py:class:: PlotModelHistory(filename: str, history: history_object, plot_metric: str = 'loss', main_branch: bool = False, epoch_best: int = None)

   Plot history of all plot_metrics (default: loss) for a training event.

   For default plot_metric and val_plot_metric are plotted. If further metrics are provided (name must somehow include
   the word `<plot_metric>`), this additional information is added to the plot with an separate y-axis scale on the
   right side (shared for all additional metrics). The plot is saved locally. For a proper saving behaviour, the
   parameter filename must include the absolute path for the plot.

   .. py:method:: _get_plot_metric(self, history, plot_metric, main_branch, correct_names=True)


   .. py:method:: _filter_columns(self, history: Dict) -> List[str]

      Select only columns named like %<plot_metric>%.

      The default metrics '<plot_metric>' and 'val_<plot_metric>' are removed too.

      :param history: a dict with at least '<plot_metric>' and 'val_<plot_metric>' as keys (can be derived from keras
          History.history)

      :return: filtered columns including all plot_metric variations except <plot_metric> and val_<plot_metric>.


   .. py:method:: _plot(self, filename: str) -> None

      Create plot.

      Plots <plot_metric> and val_<plot_metric> as default. If more plot_metrics are provided, they will be added with
      an additional yaxis on the right side. The plot is saved in filename.

      :param filename: name (including total path) of the plot to save.



.. py:class:: PlotModelLearningRate(filename: str, lr_sc: lr_object)

   Plot the behaviour of the learning rate in dependence of the number of epochs.

   The plot is saved locally as pdf. For a proper saving behaviour, the parameter filename must include the absolute
   path for the plot.

   .. py:method:: _plot(self, filename: str) -> None

      Create plot.

      Plot the learning rate in dependence of epoch.

      :param filename: name (including total path) of the plot to save.