rise_image
==========

.. py:module:: rise_image


Classes
-------

.. autoapisummary::

   rise_image.RISEImage


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

.. py:class:: RISEImage(n_masks=1000, feature_res=8, p_keep=None, axis_labels=None, preprocess_function=None)

   RISE implementation for images based on https://github.com/eclique/RISE/blob/master/Easy_start.ipynb.


   .. py:attribute:: n_masks


   .. py:attribute:: feature_res


   .. py:attribute:: p_keep


   .. py:attribute:: preprocess_function


   .. py:attribute:: masks
      :value: None



   .. py:attribute:: predictions
      :value: None



   .. py:attribute:: axis_labels


   .. py:method:: explain(model_or_function, input_data, labels, batch_size=100)

      Runs the RISE explainer on images.

         The model will be called with masked images,
         with a shape defined by `batch_size` and the shape of `input_data`.

      :param model_or_function: The function that runs the model to be explained _or_
                                the path to a ONNX model on disk.
      :type model_or_function: callable or str
      :param input_data: Image to be explained
      :type input_data: np.ndarray
      :param batch_size: Batch size to use for running the model.
      :type batch_size: int
      :param labels: Labels to be explained
      :type labels: Iterable(int)

      :returns: Explanation heatmap for each class (np.ndarray).



   .. py:method:: _prepare_input_data_and_model(input_data, model_or_function)

      Prepares the input data as an xarray with an added batch dimension and creates a preprocessing function.



   .. py:method:: _set_axis_labels(input_data)


   .. py:method:: _determine_p_keep(input_data, runner, n_masks=100)

      See n_mask default value https://github.com/dianna-ai/dianna/issues/24#issuecomment-1000152233.



   .. py:method:: _calculate_max_class_std(p_keep, runner, input_data, n_masks)


   .. py:method:: _prepare_image_data(input_data)

      Transforms the data to be of the shape and type RISE expects.

      :param input_data: Data to be explained
      :type input_data: xarray

      :returns: transformed input data, preprocessing function to use with utils.get_function()



   .. py:method:: _get_full_preprocess_function(channel_axis_index, dtype)

      Creates a full preprocessing function.

      Creates a preprocessing function that incorporates both the (optional) user's
      preprocessing function, as well as any needed dtype and shape conversions

      :param channel_axis_index: Axis index of the channels in the input data
      :type channel_axis_index: int
      :param dtype: Data type of the input data (e.g. np.float32)
      :type dtype: type

      :returns: Function that first ensures the data has the same shape and type as the input data,
                then runs the users' preprocessing function