:py:mod:`orcanet.lib.label_modifiers`
=====================================

.. py:module:: orcanet.lib.label_modifiers


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

Classes
~~~~~~~

.. autoapisummary::

   orcanet.lib.label_modifiers.ColumnLabels
   orcanet.lib.label_modifiers.RegressionLabels
   orcanet.lib.label_modifiers.RegressionLabelsSplit
   orcanet.lib.label_modifiers.ClassificationLabels
   orcanet.lib.label_modifiers.TSClassifier




.. py:class:: ColumnLabels(model)

   
   Label of each model output is column with the same name in the h5 file.
   This is the default label modifier.


   :Parameters:

       **model** : ks.Model
           A keras model.














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.. py:class:: RegressionLabels(columns, model_output=None, log10=False, stacks=None)

   
   Generate labels for regression.


   :Parameters:

       **columns** : str or list
           Name(s) of the columns in the label dataset that contain the labels.

       **model_output** : str, optional
           Name of the output of the network.
           Default: Same as columns (only valid if columns is a str).

       **log10** : bool
           Take log10 of the labels. Invalid values in the label will produce 0
           and a warning.

       **stacks** : int, optional
           Stack copies of the label this many times along a new axis at position 1.
           E.g. if the label is shape (?, 3), it will become
           shape (?, stacks, 3). Used for lkl regression.











   .. rubric:: Examples

   >>> RegressionLabels(columns=['dir_x', 'dir_y', 'dir_z'], model_output='dir')
   or in the config.toml:
   label_modifier = {name='RegressionLabels', columns=['dir_x','dir_y','dir_z'], model_output='dir'}
   Will produce array of shape (bs, 3) for model output 'dir'.
   >>> RegressionLabels(columns='dir_x')
   Will produce array of shape (bs, 1) for model output 'dir_x'.



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   .. py:method:: process_label(y_value)



.. py:class:: RegressionLabelsSplit(*args, **kwargs)



   
   Generate labels for regression.

   Intended for networks that output recos and errs in seperate towers
   (for example when using OutputRegNormalSplit as output layer block).















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.. py:class:: ClassificationLabels(column, classes, model_output=None)

   
   One-hot encoding for general purpose classification labels based on one mc label column.


   :Parameters:

       **column** : str
           Identifier of which mc info to create the labels from.

       **classes** : dict
           Specify for each class the conditions the column name has to fulfil.
           The keys have to be named "class1", "class2", etc

       **model_output** : str, optional
           The name of the output layer's outputs.














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.. py:class:: TSClassifier(is_cc_convention, model_output='ts_output')

   
   One-hot encoding for track/shower classifier. Muon neutrino CC are tracks, the rest
   of neutrinos is shower. This means, this has to be extended for tau neutrinos. Atm.
   muon events, if any, are tracks.


   :Parameters:

       **is_cc_convention** : int
           The convention used in the MC prod to indicate a charged current interaction.
           For post 2020 productions this is 2.

       **model_output** : str, optional
           Name of the output of the network.
           Default: Same as names (only valid if names is a str).














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