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.. only:: html

    .. note::
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        Click :ref:`here <sphx_glr_download_auto_examples_plot_user_defined_stainer_example.py>`
        to download the full example code

.. rst-class:: sphx-glr-example-title

.. _sphx_glr_auto_examples_plot_user_defined_stainer_example.py:


User-defined Custom Stainers
============================

This page shows an example of how to create your own user-defined custom stainers, which subclasses from the Stainer class.

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.. code-block:: default

    from ddf.stainer import Stainer








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When creating a new stainer, it needs to inherit from the Stainer class in
ddf.stainer.

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The initialisation should include the name, row indices (if applicable) and
column indices (if applicable). If any other relevant initialisations are
required, they can be included as well.
If the row or column indices do not apply to the stainer,
an empty list can be provided to the superclass init.

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When defining the transform, the parameters to be included should be
df (the dataframe to be transformed), rng (a RNG Generator), row_idx = None and
col_idx = None.

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In the transform method, the self._init_transform(df, row_idx, col_idx) method
can be called to accurately generate the row_idx and col_idx (This allows
the Stainer to work correctly with DDF). The transform method should then
implement the Stainer.

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To provide relevant statistics to the user, messages and timings can be added.
These can be added via self.update_history(message, time)

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A row mapping and column mapping are also required. These represent a movement
or creation of any row / col in the dataframe. It should be formatted as a
dictionary where key = old_row/col_index, value = List of indices of where the 
corresponding row/col ended up in the new dataframe. For instance, if the 
index-2 row was duplicated and is now the index-2 and index-3 row,
the row_map should contain the entry {2: [2, 3]}. If the rows/columns order
were not altered, an empty dictionary should be returned.

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The transform function should return a tuple of the new dataframe, row mapping,
and the column mapping.

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Refer to the sample code below for an example. 

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.. code-block:: default


    class ShuffleStainer(Stainer):
        def __init__(self, name = "Shuffle"):   
            super().__init__(name, [], [])  # name, row_idx, col_idx
        
        def transform(self, df, rng, row_idx = None, col_idx = None):
            new_df, row_idx, col_idx = self._init_transform(df, row_idx, col_idx)

            start = time()

            """
            ## Implementation of Shuffle ##
            Creates new_df
            Creates new_idx = Original row numbers, in order of the new row numbers
            """
            pass 


            """Creates the mapping"""
            row_map = {}
            for i in range(df.shape[0]):
                row_map[new_idx[i]] = [i]

            end = time() # Timer to be added into history
            self.update_history("Order of rows randomized", end - start) # message, time
            return new_df, row_map, {} # new dataframe, row map, column map








.. rst-class:: sphx-glr-timing

   **Total running time of the script:** ( 0 minutes  0.001 seconds)


.. _sphx_glr_download_auto_examples_plot_user_defined_stainer_example.py:


.. only :: html

 .. container:: sphx-glr-footer
    :class: sphx-glr-footer-example



  .. container:: sphx-glr-download sphx-glr-download-python

     :download:`Download Python source code: plot_user_defined_stainer_example.py <plot_user_defined_stainer_example.py>`



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     :download:`Download Jupyter notebook: plot_user_defined_stainer_example.ipynb <plot_user_defined_stainer_example.ipynb>`


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