lime_tabular
============

.. py:module:: lime_tabular

.. autoapi-nested-parse::

   LIME tabular explainer.



Classes
-------

.. autoapisummary::

   lime_tabular.LIMETabular


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

.. py:class:: LIMETabular(training_data: numpy.array, mode: str = 'classification', feature_names: List[int] = None, categorical_features: List[int] = None, kernel_width: int = 25, kernel: callable = None, verbose: bool = False, class_names: List[str] = None, feature_selection: str = 'auto', random_state: int = None, **kwargs)

   Wrapper around the LIME explainer for tabular data.


   .. py:attribute:: mode
      :value: 'classification'



   .. py:attribute:: num_features


   .. py:attribute:: explainer


   .. py:method:: explain(model_or_function: Union[str, callable], input_tabular: numpy.array, labels: Optional[Iterable[int]] = None, num_samples: int = 5000, **kwargs) -> numpy.array

      Run the LIME explainer.

      :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_tabular: Data to be explained.
      :type input_tabular: np.ndarray
      :param labels: Indices of classes to be explained.
      :type labels: Iterable(int)
      :param num_samples: Number of samples
      :type num_samples: int, optional
      :param kwargs: These parameters are passed on

      Other keyword arguments: see the documentation for LimeTabularExplainer.explain_instance:
      https://lime-ml.readthedocs.io/en/latest/lime.html#lime.lime_tabular.LimeTabularExplainer.explain_instance

      :returns: An array (np.ndarray) containing LIME explanations for each class.