force_plots
================================================

.. py:module:: evalml.model_understanding.force_plots

.. autoapi-nested-parse::

   Force plots.



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



Functions
~~~~~~~~~

.. autoapisummary::
   :nosignatures:

   evalml.model_understanding.force_plots.force_plot
   evalml.model_understanding.force_plots.graph_force_plot



Contents
~~~~~~~~~~~~~~~~~~~
.. py:function:: force_plot(pipeline, rows_to_explain, training_data, y)

   Function to generate the data required to build a force plot.

   :param pipeline: The pipeline to generate the force plot for.
   :type pipeline: PipelineBase
   :param rows_to_explain: A list of the indices of the training_data to explain.
   :type rows_to_explain: list[int]
   :param training_data: The data used to train the pipeline.
   :type training_data: pandas.DataFrame
   :param y: The target data.
   :type y: pandas.Series

   :returns:

             list of dictionaries where each dict contains force plot data.  Each dictionary
                 entry represents the explanations for a single row.

                 For single row binary force plots:
                     [{'malignant': {'expected_value': 0.37,
                                     'feature_names': ['worst concave points', 'worst perimeter', 'worst radius'],
                                     'shap_values': [0.09, 0.09, 0.08],
                                     'plot': AdditiveForceVisualizer}]

                 For two row binary force plots:
                     [{'malignant': {'expected_value': 0.37,
                                     'feature_names': ['worst concave points', 'worst perimeter', 'worst radius'],
                                     'shap_values': [0.09, 0.09, 0.08],
                                     'plot': AdditiveForceVisualizer},
                     {'malignant': {'expected_value': 0.29,
                                     'feature_names': ['worst concave points', 'worst perimeter', 'worst radius'],
                                     'shap_values': [0.05, 0.03, 0.02],
                                     'plot': AdditiveForceVisualizer}]
   :rtype: list[dict]

   :raises TypeError: If rows_to_explain is not a list.
   :raises TypeError: If all values in rows_to_explain aren't integers.


.. py:function:: graph_force_plot(pipeline, rows_to_explain, training_data, y, matplotlib=False)

   Function to generate force plots for the desired rows of the training data.

   :param pipeline: The pipeline to generate the force plot for.
   :type pipeline: PipelineBase
   :param rows_to_explain: A list of the indices indicating which of the rows of
                           the training_data to explain.
   :type rows_to_explain: list[int]
   :param training_data: The data used to train the pipeline.
   :type training_data: pandas.DataFrame
   :param y: The target data for the pipeline.
   :type y: pandas.Series
   :param matplotlib: flag to display the force plot using matplotlib (outside of jupyter)
                      Defaults to False.
   :type matplotlib: bool

   :returns:

             The same as force_plot(), but with an additional
                 key in each dictionary for the plot.
   :rtype: list[dict[shap.AdditiveForceVisualizer]]