:py:mod:`calibration`
=====================

.. py:module:: calibration


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


Functions
~~~~~~~~~

.. autoapisummary::

   calibration.get_p_Y_val_approx_mahalanobis
   calibration.plot_calibration



.. py:function:: get_p_Y_val_approx_mahalanobis(post_samples, y_mean, y_truth, cov)

   Calculate the percentage of draws from the predicted distribution that
   encompasses the truth, for all of the examples in the validation set.

   Parameters
   ----------
   post_samples : np.array of shape [n_samples, n_sightlines, Y_dim]
       BNN posterior samples
   y_mean : np.array of shape [n_lenses, Y_dim]
       Central prediction to use in the distance calculation
   y_truth: np.array of shape [n_lenses, Y_dim]
       True values
   cov : float
       Scale factor to use in the distance calculation

   Notes
   -----
   Adapted from https://github.com/swagnercarena/ovejero



.. py:function:: plot_calibration(post_samples, y_mean, y_truth, cov, color_map=['#377eb8', '#4daf4a'], n_perc_points=20, figure=None, ls='--', legend=None, show_plot=True, block=True, title=None, dpi=200)

   Plot the calibration metric for a grid of p_X percentages,
   with error bars
   obtained through jackknife sampling

   Parameters
   ----------
   See the docstring for `get_p_Y_val_approx_mahalanobis`.
   n_perc_points : int
       Grid size of p_X (probability volume) to compare p_Y against

   Notes
   -----
   Adapted from https://github.com/swagnercarena/ovejero